Sample records for disease-drug network based
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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
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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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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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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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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 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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[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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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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Network-based Approaches in Pharmacology.
Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier
2017-10-01
In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases
Directory of Open Access Journals (Sweden)
Peng Lu
2018-01-01
Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.
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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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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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Jin, Nana; Wu, Deng; Gong, Yonghui; Bi, Xiaoman; Jiang, Hong; Li, Kongning; Wang, Qianghu
2014-01-01
An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches. PMID:25243127
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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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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.
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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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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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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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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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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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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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The current state of GPCR-based drug discovery to treat metabolic disease.
Sloop, Kyle W; Emmerson, Paul J; Statnick, Michael A; Willard, Francis S
2018-02-02
One approach of modern drug discovery is to identify agents that enhance or diminish signal transduction cascades in various cell types and tissues by modulating the activity of GPCRs. This strategy has resulted in the development of new medicines to treat many conditions, including cardiovascular disease, psychiatric disorders, HIV/AIDS, certain forms of cancer and Type 2 diabetes mellitus (T2DM). These successes justify further pursuit of GPCRs as disease targets and provide key learning that should help guide identifying future therapeutic agents. This report reviews the current landscape of GPCR drug discovery with emphasis on efforts aimed at developing new molecules for treating T2DM and obesity. We analyse historical efforts to generate GPCR-based drugs to treat metabolic disease in terms of causal factors leading to success and failure in this endeavour. © 2018 The British Pharmacological Society.
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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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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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Drug safety data mining with a tree-based scan statistic.
Kulldorff, Martin; Dashevsky, Inna; Avery, Taliser R; Chan, Arnold K; Davis, Robert L; Graham, David; Platt, Richard; Andrade, Susan E; Boudreau, Denise; Gunter, Margaret J; Herrinton, Lisa J; Pawloski, Pamala A; Raebel, Marsha A; Roblin, Douglas; Brown, Jeffrey S
2013-05-01
In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs. Copyright © 2013 John Wiley & Sons, Ltd.
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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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MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.
Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan
2017-01-01
Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any
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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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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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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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Peng, Wei; Lan, Wei; Zhong, Jiancheng; Wang, Jianxin; Pan, Yi
2017-07-15
MicroRNAs have been reported to have close relationship with diseases due to their deregulation of the expression of target mRNAs. Detecting disease-related microRNAs is helpful for disease therapies. With the development of high throughput experimental techniques, a large number of microRNAs have been sequenced. However, it is still a big challenge to identify which microRNAs are related to diseases. Recently, researchers are interesting in combining multiple-biological information to identify the associations between microRNAs and diseases. In this work, we have proposed a novel method to predict the microRNA-disease associations based on four biological properties. They are microRNA, disease, gene and environment factor. Compared with previous methods, our method makes predictions not only by using the prior knowledge of associations among microRNAs, disease, environment factors and genes, but also by using the internal relationship among these biological properties. We constructed four biological networks based on the similarity of microRNAs, diseases, environment factors and genes, respectively. Then random walking was implemented on the four networks unequally. In the walking course, the associations can be inferred from the neighbors in the same networks. Meanwhile the association information can be transferred from one network to another. The results of experiment showed that our method achieved better prediction performance than other existing state-of-the-art methods. Copyright © 2017 Elsevier Inc. All rights reserved.
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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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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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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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Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen
2015-03-01
The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.
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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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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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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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Carpenter, Kristy A; Huang, Xudong
2018-06-07
Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. The study aims to review ML-based methods used for VS and applications to Alzheimer's disease (AD) drug discovery. To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). All techniques have found success in VS, but the future of VS is likely to lean more heavily toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics of for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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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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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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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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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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Recurrent neural network based hybrid model for reconstructing gene regulatory network.
Raza, Khalid; Alam, Mansaf
2016-10-01
One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.
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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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Celiac Disease and Drug-Based Therapies: Inquiry into Patients Demands.
Branchi, Federica; Tomba, Carolina; Ferretti, Francesca; Norsa, Lorenzo; Roncoroni, Leda; Bardella, Maria Teresa; Conte, Dario; Elli, Luca
2016-01-01
Medical research is looking for alternative drug-based options to the gluten-free diet (GFD) for celiac disease. We aimed at evaluating the need for alternative therapies perceived by celiac patients. During the 2013 meeting of the Lombardy section of the Italian Celiac Patients Association, adult subjects were invited to fill in a questionnaire investigating their clinical profile in relation to compliance to the diet, quality of life (QOL) as well as their opinion on alternative therapies. Three hundred and seventy two patients (76 m, mean age 41.7 ± 13.9 years) completed the questionnaire. Patients reported a significant improvement in health status (HS) and QOL after the diet was started (p < 0.001). The GFD was accepted by 88% patients, but the need for alternative therapies was reported by 65%. Subjects expressing the need for a drug-based therapy showed a lower increase in QOL (p = 0.003) and HS (p = 0.005) on GFD. The preferred option for an alternative therapy was the use of enzymes (145 subjects), followed by a vaccine (111 subjects). The GFD is favorably accepted by most celiac patients. Nevertheless, a proportion of patients pronounce themselves in favor of the development of alternative drugs. © 2016 S. Karger AG, Basel.
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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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Wen, Ming Ming; El-Salamouni, Noha S; El-Refaie, Wessam M; Hazzah, Heba A; Ali, Mai M; Tosi, Giovanni; Farid, Ragwa M; Blanco-Prieto, Maria J; Billa, Nashiru; Hanafy, Amira S
2017-01-10
Alzheimer's disease (AD) is a neurodegenerative disease with high prevalence in the rapidly growing elderly population in the developing world. The currently FDA approved drugs for the management of symptomatology of AD are marketed mainly as conventional oral medications. Due to their gastrointestinal side effects and lack of brain targeting, these drugs and dosage regiments hinder patient compliance and lead to treatment discontinuation. Nanotechnology-based drug delivery systems (NTDDS) administered by different routes can be considered as promising tools to improve patient compliance and achieve better therapeutic outcomes. Despite extensive research, literature screening revealed that clinical activities involving NTDDS application in research for AD are lagging compared to NTDDS for other diseases such as cancers. The industrial perspectives, processability, and cost/benefit ratio of using NTDDS for AD treatment are usually overlooked. Moreover, active and passive immunization against AD are by far the mostly studied alternative AD therapies because conventional oral drug therapy is not yielding satisfactorily results. NTDDS of approved drugs appear promising to transform this research from 'paper to clinic' and raise hope for AD sufferers and their caretakers. This review summarizes the recent studies conducted on NTDDS for AD treatment, with a primary focus on the industrial perspectives and processability. Additionally, it highlights the ongoing clinical trials for AD management. Copyright © 2016 Elsevier B.V. All rights reserved.
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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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Huang, Hao; He, Yuehan; 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 biologi...
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Lifescience Database Archive (English)
Full Text Available 18031251 Toll-like receptor (TLR)-based networks regulate neutrophilic inflammation inrespiratory...l) (.csml) Show Toll-like receptor (TLR)-based networks regulate neutrophilic inflammation inrespiratory dis...utrophilic inflammation inrespiratory disease. Authors Sabroe I, Whyte MK. Publication Biochem Soc Trans. 20
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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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Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Directory of Open Access Journals (Sweden)
Srdjan Sladojevic
2016-01-01
Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.
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Social networks, web-based tools and diseases: implications for biomedical research.
Costa, Fabricio F
2013-03-01
Advances in information technology have improved our ability to gather, collect and analyze information from individuals online. Social networks can be seen as a nonlinear superposition of a multitude of complex connections between people where the nodes represent individuals and the links between them capture a variety of different social interactions. The emergence of different types of social networks has fostered connections between individuals, thus facilitating data exchange in a variety of fields. Therefore, the question posed now is "can these same tools be applied to life sciences in order to improve scientific and medical research?" In this article, I will review how social networks and other web-based tools are changing the way we approach and track diseases in biomedical research. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo; Tan, Aik Choon
2018-01-01
Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.
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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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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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Network-based analysis of proteomic profiles
Wong, Limsoon
2016-01-26
Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.
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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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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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Protein-Based Drug-Delivery Materials
Jao, Dave; Xue, Ye; Medina, Jethro; Hu, Xiao
2017-01-01
There is a pressing need for long-term, controlled drug release for sustained treatment of chronic or persistent medical conditions and diseases. Guided drug delivery is difficult because therapeutic compounds need to survive numerous transport barriers and binding targets throughout the body. Nanoscale protein-based polymers are increasingly used for drug and vaccine delivery to cross these biological barriers and through blood circulation to their molecular site of action. Protein-based pol...
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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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Munneke, M.; Nijkrake, M.J.; Keus, S.H.J.; Kwakkel, G.; Berendse, H.W.; Roos, R.A.; Borm, G.F.; Adang, E.M.M.; Overeem, S.; Bloem, B.R.
2010-01-01
BACKGROUND: Many patients with Parkinson's disease are treated with physiotherapy. We have developed a community-based professional network (ParkinsonNet) that involves training of a selected number of expert physiotherapists to work according to evidence-based recommendations, and structured
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Munneke, M.; Nijkrake, M.J.; Keus, S.H.; Kwakkel, G.; Berendse, H.W.; Roos, R.A.; Borm, G.F.; Adang, E.M.; Overeem, S.; Bloem, B.R.
2010-01-01
Background: Many patients with Parkinson's disease are treated with physiotherapy. We have developed a community-based professional network (ParkinsonNet) that involves training of a selected number of expert physiotherapists to work according to evidence-based recommendations, and structured
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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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Zhang, Han; Yan, Zhi-Yang; Wang, Yu-Xi; Bai, Ming; Wang, Xiao-Bo; Huang, Xiao-Xiao; Song, Shao-Jiang
2018-02-16
Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with synaptic dysfunction, pathological accumulation of β-amyloid (Aβ), and neuronal loss. Given the prevalence of AD and the lack of effective long-term therapies, there is a pressing need to discover viable leads that can be developed into clinically approved drugs with disease-modifying effects. The analysis of current reported literatures confirms the importance of the plants of Pithecellobium genus as candidate against AD. Hence, it is necessary to identify selective anti-dementia agents from this genus. To explore potential compounds with marked effect on AD in Pithecellobium genus, a compound database based on the methods of network pharmacology prediction was established in this paper by constructing the compound-disease target network. The result showed that the most effective compound in the plants of this genus might be (7'R,8'R)-7'-methoxyl strebluslignanol, and the most potential target might be Macrophage colony-stimulating factor 1 receptor.
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Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E.; Re, Matteo
2014-01-01
Objective In the context of “network medicine”, gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. Materials and methods We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. Results The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different “informativeness” embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Conclusions Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further
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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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FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE.
Prasad, Gautam; Joshi, Shantanu H; Nir, Talia M; Toga, Arthur W; Thompson, Paul M
2013-01-01
We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A flow network is first created using a lattice graph by connecting all lattice points (voxel centers) to all their neighbors by edges. Edge weights are based on the orientation distribution function (ODF) value in the direction of the edge. The maximum-flow is computed based on this flow graph using the flow or the capacity between each region of interest (ROI) pair by following the connected tractography fibers projected onto the flow graph edges. Network measures such as global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity are computed from the flow connectivity matrix. We applied our method to diffusion-weighted images (DWIs) from 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD) and segmented co-registered anatomical MRIs into cortical regions. Experimental results showed better performance compared to the standard fiber-counting methods when distinguishing Alzheimer's disease from normal aging.
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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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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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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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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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PEG-based degradable networks for drug delivery applications
Ostroha, Jamie L.
The controlled delivery of therapeutic agents by biodegradable hydrogels has become a popular mechanism for drug administration in recent years. Hydrogels are three-dimensional networks of polymer chains held together by crosslinks. Although the changes which the hydrogel undergoes in solution are important to a wide range of experimental studies, they have not been investigated systematically and the factors which influence the degree of swelling have not been adequately described. Hydrogels made of poly(ethylene glycol) (PEG) will generally resist degradation in aqueous conditions, while a hydrogel made from a copolymer of poly(lactic acid) (PLA) and PEG will degrade via hydrolysis of the lactic acid group. This ability to degrade makes these hydrogels promising candidates for controlled release drug delivery systems. The goal of this research was to characterize the swelling and degradation of both degradable and non-degradable gels and to evaluate the release of different drugs from these hydrogels, where the key variable is the molecular weight of the PEG segment. These hydrogels were formed by the addition and subsequent chemically crosslinking of methacrylate end groups. During crosslinking, both PEG and LA-PEG-LA hydrogels of varied PEG molecular weight were loaded with Vitamin B12, Insulin, Haloperidol, and Dextran. It was shown that increasing PEG molecular weight produces a hydrogel with larger pores, thus increasing water uptake and degradation rate. While many environmental factors do not affect the swelling behavior, they do significantly impact the degradation of the hydrogel, and thus the release of incorporated therapeutic agents.
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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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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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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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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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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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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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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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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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Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo
2014-06-01
In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both
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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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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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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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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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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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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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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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A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity
Directory of Open Access Journals (Sweden)
Ted G Laderas
2015-12-01
Full Text Available Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, among other cancer hallmarks. High throughput omics techniques are used in precision medicine, allowing identification of these mutations with the goal of identifying treatments that target them. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to its dysregulation, a new genomic feature that we term surrogate oncogenes. By mapping mutations to a protein/protein interaction network, we can determine significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified significant surrogate oncogenes in oncogenes such as BRCA1 and ESR1. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations on an individual level. Our model has the potential for integrating patient-unique mutations in predicting drug-sensitivity, suggesting a potential new direction in precision medicine, as well as a new approach for drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers within the Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue types.
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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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Casamento, K.; Laverty, A.; Wilsher, M.; Twiss, J.; Gabbay, E.; Glaspole, I.; Jaffe, A.
2016-01-01
Background We investigated the feasibility of using an online registry to provide prevalence data for multiple orphan lung diseases in Australia and New Zealand. Methods A web-based registry, The Australasian Registry Network of Orphan Lung Diseases (ARNOLD) was developed based on the existing British Paediatric Orphan Lung Disease Registry. All adult and paediatric respiratory physicians who were members of the Thoracic Society of Australia and New Zealand in Australia and New Zealand were s...
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Singh, Baljit; Sharma, Vikrant
2014-01-30
The present article deals with design of tragacanth gum-based pH responsive hydrogel drug delivery systems. The characterization of hydrogels has been carried out by SEMs, EDAX, FTIR, (13)C NMR, XRD, TGA/DTA/DTG and swelling studies. The correlation between reaction conditions and structural parameters of polymer networks such as polymer volume fraction in the swollen state (ϕ), Flory-Huggins interaction parameter (χ), molecular weight of the polymer chain between two neighboring cross links (M¯c), crosslink density (ρ) and mesh size (ξ) has been determined. The different kinetic models such as zero order, first order, Higuchi square root law, Korsmeyer-Peppas model and Hixson-Crowell cube root model were applied and it has been observed that release profile of amoxicillin best followed the first order model for the release of drug from the polymer matrix. The swelling of the hydrogels and release of drug from the drug loaded hydrogels occurred through non-Fickian diffusion mechanism in pH 7.4 solution. Copyright © 2013 Elsevier Ltd. All rights reserved.
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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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Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network
Yao, Qianlan; Xu, Yanjun; Yang, Haixiu; Shang, Desi; Zhang, Chunlong; Zhang, Yunpeng; Sun, Zeguo; Shi, Xinrui; Feng, Li; Han, Junwei; Su, Fei; Li, Chunquan; Li, Xia
2015-01-01
The identification of disease-related metabolites is important for a better understanding of metabolite pathological processes in order to improve human medicine. Metabolites, which are the terminal products of cellular regulatory process, can be affected by multi-omic processes. In this work, we propose a powerful method, MetPriCNet, to predict and prioritize disease candidate metabolites based on integrated multi-omics information. MetPriCNet prioritized candidate metabolites based on their global distance similarity with seed nodes in a composite network, which integrated multi-omics information from the genome, phenome, metabolome and interactome. After performing cross-validation on 87 phenotypes with a total of 602 metabolites, MetPriCNet achieved a high AUC value of up to 0.918. We also assessed the performance of MetPriCNet on 18 disease classes and found that 4 disease classes achieved an AUC value over 0.95. Notably, MetPriCNet can also predict disease metabolites without known disease metabolite knowledge. Some new high-risk metabolites of breast cancer were predicted, although there is a lack of known disease metabolite information. A predicted disease metabolic landscape was constructed and analyzed based on the results of MetPriCNet for 87 phenotypes to help us understand the genetic and metabolic mechanism of disease from a global view. PMID:26598063
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Nanotechnology-based drug delivery systems
Directory of Open Access Journals (Sweden)
Singh Baljit
2007-12-01
Full Text Available Abstract Nanoparticles hold tremendous potential as an effective drug delivery system. In this review we discussed recent developments in nanotechnology for drug delivery. To overcome the problems of gene and drug delivery, nanotechnology has gained interest in recent years. Nanosystems with different compositions and biological properties have been extensively investigated for drug and gene delivery applications. To achieve efficient drug delivery it is important to understand the interactions of nanomaterials with the biological environment, targeting cell-surface receptors, drug release, multiple drug administration, stability of therapeutic agents and molecular mechanisms of cell signalling involved in pathobiology of the disease under consideration. Several anti-cancer drugs including paclitaxel, doxorubicin, 5-fluorouracil and dexamethasone have been successfully formulated using nanomaterials. Quantom dots, chitosan, Polylactic/glycolic acid (PLGA and PLGA-based nanoparticles have also been used for in vitro RNAi delivery. Brain cancer is one of the most difficult malignancies to detect and treat mainly because of the difficulty in getting imaging and therapeutic agents past the blood-brain barrier and into the brain. Anti-cancer drugs such as loperamide and doxorubicin bound to nanomaterials have been shown to cross the intact blood-brain barrier and released at therapeutic concentrations in the brain. The use of nanomaterials including peptide-based nanotubes to target the vascular endothelial growth factor (VEGF receptor and cell adhesion molecules like integrins, cadherins and selectins, is a new approach to control disease progression.
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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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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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Giordano, Carmen; Albani, Diego; Gloria, Antonio; Tunesi, Marta; Batelli, Sara; Russo, Teresa; Forloni, Gianluigi; Ambrosio, Luigi; Cigada, Alberto
2009-12-01
This review presents two intriguing multidisciplinary strategies that might make the difference in the treatment of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. The first proposed strategy is based on the controlled delivery of recombinant proteins known to play a key role in these neurodegenerative disorders that are released in situ by optimized polymer-based systems. The second strategy is the use of engineered cells, encapsulated and delivered in situ by suitable polymer-based systems, that act as drug reservoirs and allow the delivery of selected molecules to be used in the treatment of Alzheimer's and Parkinson's diseases. In both these scenarios, the design and development of optimized polymer-based drug delivery and cell housing systems for central nervous system applications represent a key requirement. Materials science provides suitable hydrogel-based tools to be optimized together with suitably designed recombinant proteins or drug delivering-cells that, once in situ, can provide an effective treatment for these neurodegenerative disorders. In this scenario, only interdisciplinary research that fully integrates biology, biochemistry, medicine and materials science can provide a springboard for the development of suitable therapeutic tools, not only for the treatment of Alzheimer's and Parkinson's diseases but also, prospectively, for a wide range of severe neurodegenerative disorders.
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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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Parham, Kourosh; Chapurin, Nikita; Schulz, Kris; Shin, Jennifer J.; Pynnonen, Melissa A.; Witsell, David L.; Langman, Alan; Nguyen-Huynh, Anh; Ryan, Sheila E.; Vambutas, Andrea; Wolfley, Anne; Roberts, Rhonda; Lee, Walter T.
2017-01-01
Objectives 1) Describe thyroid-related diagnoses and procedures in CHEER across academic and community sites. 2) Compare management of malignant thyroid disease across these sites, and 3) Provide practice based data related to flexible laryngoscopy vocal fold assessment before and after thyroid surgery based on AAO-HNSF Clinical Practice Guidelines. Study Design Review of retrospective data collection (RDC) database of the CHEER network using ICD-9 and CPT codes related to thyroid conditions. Setting Multisite practice based network. Subjects and Methods There were 3,807 thyroid patients (1,392 malignant; 2,415 benign) with 10,160 unique visits identified from 1 year of patient data in the RDC. Analysis was performed for identified cohort of patients using demographics, site characteristics and diagnostic and procedural distribution. Results Mean number of patients with thyroid disease per site was 238 (range 23–715). In community practices, 19% of patients with thyroid disease had cancer versus 45% in the academic setting (pVocal fold function was assessed by flexible laryngoscopy in 34.0% of pre-operative patients and in 3.7% post-operatively. Conclusion This is the first overview of malignant and benign thyroid disease through CHEER. It shows how the RDC can be used alone and with national guidelines to inform of clinical practice patterns in academic and community sites. This demonstrates the potential for future thyroid related studies utilizing the Otolaryngology-H&N Surgery’s practice-based research network. PMID:27371622
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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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Protein-Based Drug-Delivery Materials
Directory of Open Access Journals (Sweden)
Dave Jao
2017-05-01
Full Text Available There is a pressing need for long-term, controlled drug release for sustained treatment of chronic or persistent medical conditions and diseases. Guided drug delivery is difficult because therapeutic compounds need to survive numerous transport barriers and binding targets throughout the body. Nanoscale protein-based polymers are increasingly used for drug and vaccine delivery to cross these biological barriers and through blood circulation to their molecular site of action. Protein-based polymers compared to synthetic polymers have the advantages of good biocompatibility, biodegradability, environmental sustainability, cost effectiveness and availability. This review addresses the sources of protein-based polymers, compares the similarity and differences, and highlights characteristic properties and functionality of these protein materials for sustained and controlled drug release. Targeted drug delivery using highly functional multicomponent protein composites to guide active drugs to the site of interest will also be discussed. A systematical elucidation of drug-delivery efficiency in the case of molecular weight, particle size, shape, morphology, and porosity of materials will then be demonstrated to achieve increased drug absorption. Finally, several important biomedical applications of protein-based materials with drug-delivery function—including bone healing, antibiotic release, wound healing, and corneal regeneration, as well as diabetes, neuroinflammation and cancer treatments—are summarized at the end of this review.
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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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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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Discovery of Boolean metabolic networks: integer linear programming based approach.
Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing
2018-04-11
Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".
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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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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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A pathway-based network analysis of hypertension-related genes
Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng
2016-02-01
Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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
means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.
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Wang, Yongcui; Chen, Shilong; Deng, Naiyang; Wang, Yong
2013-01-01
Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. PMID:24244318
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The soft computing-based approach to investigate allergic diseases: a systematic review.
Tartarisco, Gennaro; Tonacci, Alessandro; Minciullo, Paola Lucia; Billeci, Lucia; Pioggia, Giovanni; Incorvaia, Cristoforo; Gangemi, Sebastiano
2017-01-01
Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field. The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases. The review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016. The review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause-effect relationships, which make it difficult to assess the evolution of disease. Although most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.
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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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A link prediction method for heterogeneous networks based on BP neural network
Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu
2018-04-01
Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.
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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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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.
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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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Identifying noncoding risk variants using disease-relevant gene regulatory networks.
Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai
2018-02-16
Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.
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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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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.
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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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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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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
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Albumin-based drug delivery: harnessing nature to cure disease.
Larsen, Maja Thim; Kuhlmann, Matthias; Hvam, Michael Lykke; Howard, Kenneth A
2016-01-01
The effectiveness of a drug is dependent on accumulation at the site of action at therapeutic levels, however, challenges such as rapid renal clearance, degradation or non-specific accumulation requires drug delivery enabling technologies. Albumin is a natural transport protein with multiple ligand binding sites, cellular receptor engagement, and a long circulatory half-life due to interaction with the recycling neonatal Fc receptor. Exploitation of these properties promotes albumin as an attractive candidate for half-life extension and targeted intracellular delivery of drugs attached by covalent conjugation, genetic fusions, association or ligand-mediated association. This review will give an overview of albumin-based products with focus on the natural biological properties and molecular interactions that can be harnessed for the design of a next-generation drug delivery platform.
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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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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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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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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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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.
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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....
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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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Directory of Open Access Journals (Sweden)
Wei Kong
2014-01-01
Full Text Available Alzheimer’s disease (AD is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA, which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.
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Do nonsteroidal anti-inflammatory drugs decrease the risk for Alzheimer's disease?
DEFF Research Database (Denmark)
Andersen, K; Launer, L J; Ott, A
1995-01-01
Based on reports that the use of nonsteroidal anti-inflammatory drugs (NSAIDs) may reduce the risk for Alzheimer's disease (AD), we studied the cross-sectional relation between NSAID use and the risk for AD in a population-based study of disease and disability in older people. After controlling...
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Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin
2013-03-01
Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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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.
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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.
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Kim, Han Byul; Lee, Woong Woo; Kim, Aryun; Lee, Hong Ji; Park, Hye Young; Jeon, Hyo Seon; Kim, Sang Kyong; Jeon, Beomseok; Park, Kwang S
2018-04-01
Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the use of a convolutional neural network (CNN) to differentiate the severity of symptoms as measured in data collected from a wearable device. Tremor signals were recorded from 92 PD patients using a custom-developed device (SNUMAP) equipped with an accelerometer and gyroscope mounted on a wrist module. Neurologists assessed the tremor symptoms on the Unified Parkinson's Disease Rating Scale (UPDRS) from simultaneously recorded video footages. The measured data were transformed into the frequency domain and used to construct a two-dimensional image for training the network, and the CNN model was trained by convolving tremor signal images with kernels. The proposed CNN architecture was compared to previously studied machine learning algorithms and found to outperform them (accuracy = 0.85, linear weighted kappa = 0.85). More precise monitoring of PD tremor symptoms in daily life could be possible using our proposed method. Copyright © 2018 Elsevier Ltd. All rights reserved.
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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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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
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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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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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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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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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Reconstruction of biological networks based on life science data integration.
Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf
2010-10-27
For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.
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Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing
2015-01-01
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 disp...
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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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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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Identifying novel drug indications through automated reasoning.
Directory of Open Access Journals (Sweden)
Luis Tari
Full Text Available With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7% are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8% are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative based on their molecular targets and interactions alone and has the potential to discover novel drug indications for
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Network topology and functional connectivity disturbances precede the onset of Huntington's disease.
Harrington, Deborah L; Rubinov, Mikail; Durgerian, Sally; Mourany, Lyla; Reece, Christine; Koenig, Katherine; Bullmore, Ed; Long, Jeffrey D; Paulsen, Jane S; Rao, Stephen M
2015-08-01
Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease
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DEFF Research Database (Denmark)
Dencker, Ditte; Thomsen, Morgane; Wörtwein, Gitta
2011-01-01
's disease and drug abuse. Dopaminergic systems are regulated by cholinergic, especially muscarinic, input. Not surprisingly, increasing evidence implicates muscarinic acetylcholine receptor-mediated pathways as potential targets for the treatment of these disorders classically viewed as "dopamine based...... site. Such agents may lead to the development of novel classes of drugs useful for the treatment of psychosis, drug abuse and Parkinson's disease. The present review highlights recent studies carried out using muscarinic receptor knock-out mice and new subtype-selective allosteric ligands to assess...... the roles of M(1), M(4), and M(5) receptors in various central processes that are under strong dopaminergic control. The outcome of these studies opens new perspectives for the use of novel muscarinic drugs for several severe disorders of the CNS....
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Disease candidate gene identification and prioritization using protein interaction networks
Directory of Open Access Journals (Sweden)
Aronow Bruce J
2009-02-01
Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.
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Prediction of drug synergy in cancer using ensemble-based machine learning techniques
Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder
2018-04-01
Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.
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Reconstruction of biological networks based on life science data integration
Directory of Open Access Journals (Sweden)
Kormeier Benjamin
2010-06-01
Full Text Available For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.
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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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Ligand cluster-based protein network and ePlatton, a multi-target ligand finder.
Du, Yu; Shi, Tieliu
2016-01-01
Small molecules are information carriers that make cells aware of external changes and couple internal metabolic and signalling pathway systems with each other. In some specific physiological status, natural or artificial molecules are used to interact with selective biological targets to activate or inhibit their functions to achieve expected biological and physiological output. Millions of years of evolution have optimized biological processes and pathways and now the endocrine and immune system cannot work properly without some key small molecules. In the past thousands of years, the human race has managed to find many medicines against diseases by trail-and-error experience. In the recent decades, with the deepening understanding of life and the progress of molecular biology, researchers spare no effort to design molecules targeting one or two key enzymes and receptors related to corresponding diseases. But recent studies in pharmacogenomics have shown that polypharmacology may be necessary for the effects of drugs, which challenge the paradigm, 'one drug, one target, one disease'. Nowadays, cheminformatics and structural biology can help us reasonably take advantage of the polypharmacology to design next-generation promiscuous drugs and drug combination therapies. 234,591 protein-ligand interactions were extracted from ChEMBL. By the 2D structure similarity, 13,769 ligand emerged from 156,151 distinct ligands which were recognized by 1477 proteins. Ligand cluster- and sequence-based protein networks (LCBN, SBN) were constructed, compared and analysed. For assisting compound designing, exploring polypharmacology and finding possible drug combination, we integrated the pathway, disease, drug adverse reaction and the relationship of targets and ligand clusters into the web platform, ePlatton, which is available at http://www.megabionet.org/eplatton. Although there were some disagreements between the LCBN and SBN, communities in both networks were largely the same
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Anwar, Hina; Ahmad, Mahmood; Minhas, Muhammad Usman; Rehmani, Sahrish
2017-06-15
A new natural and synthetic polymeric blend to form interpenetrating polymer network (IPN) hydrogels was synthesized utilizing sodium alginate and PVA as polymers by free radical polymerization employing 2-Acylamido-2-methylpropane-sulfonic acid as monomer (AMPS) and tramadol HCl as model drug through 3 2 level full factorial design to evaluate the impact of selected independent factors i.e. polymer (sodium alginate) and monomer (AMPS) contents on swelling index at 18th hour, percent drug release at 18th hour, time required for 80% drug release and drug entrapment efficiency as dependent variables. FTIR, SEM, sol-gel analysis, equilibrium swelling studies and in-vitro release kinetics were performedfor in-vitro characterization of formulated IPN hydrogels. In-vitro studies carried out at pH 1.2 and pH 7.4 revealed pH independent swelling and drug release from polymeric IPN, providing controlled drug release for an extended period of time with improved entrapment efficiency, thereby concluding that this polymeric blend may be a promising system for the prolonged drug delivery. Copyright © 2017 Elsevier Ltd. All rights reserved.
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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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Casamento, K; Laverty, A; Wilsher, M; Twiss, J; Gabbay, E; Glaspole, I; Jaffe, A
2016-04-18
We investigated the feasibility of using an online registry to provide prevalence data for multiple orphan lung diseases in Australia and New Zealand. A web-based registry, The Australasian Registry Network of Orphan Lung Diseases (ARNOLD) was developed based on the existing British Paediatric Orphan Lung Disease Registry. All adult and paediatric respiratory physicians who were members of the Thoracic Society of Australia and New Zealand in Australia and New Zealand were sent regular emails between July 2009 and June 2014 requesting information on patients they had seen with any of 30 rare lung diseases. Prevalence rates were calculated using population statistics. Emails were sent to 649 Australian respiratory physicians and 65 in New Zealand. 231 (32.4%) physicians responded to emails a total of 1554 times (average 7.6 responses per physician). Prevalence rates of 30 rare lung diseases are reported. A multi-disease rare lung disease registry was implemented in the Australian and New Zealand health care settings that provided prevalence data on orphan lung diseases in this region but was limited by under reporting.
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Directory of Open Access Journals (Sweden)
Sidhartha Chaudhury
Full Text Available In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of
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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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Directory of Open Access Journals (Sweden)
Tai B
2014-07-01
Full Text Available Betty Tai, Steven Sparenborg, Udi E Ghitza, David Liu Center for the Clinical Trials Network, National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland, USA Abstract: The Patient Protection and Affordable Care Act (2010 and the Mental Health Parity and Addiction Equity Act (2008 expand substance use disorder (SUD care services in the USA into general medical settings. Care offered in these settings will engage substance-using patients in an integrated and patient-centered environment that addresses physical and mental health comorbidities and follows a chronic care model. This expansion of SUD services presents a great need for evidence-based practices useful in general medical settings, and reveals several research gaps to be addressed. The National Drug Abuse Treatment Clinical Trials Network of the National Institute on Drug Abuse can serve an important role in this endeavor. High-priority research gaps are highlighted in this commentary. A discussion follows on how the National Drug Abuse Treatment Clinical Trials Network can transform to address changing patterns in SUD care to efficiently generate evidence to guide SUD treatment practice within the context of recent US health care legislation. Keywords: Patient Protection and Affordable Care Act, National Drug Abuse Treatment Clinical Trials Network, substance use disorders, practice-based research network, electronic health records
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Sehgal, Sheikh Arslan; Hammad, Mirza A; Tahir, Rana Adnan; Akram, Hafiza Nisha; Ahmad, Faheem
2018-03-15
As the number of elderly persons increases, neurodegenerative diseases are becoming ubiquitous. There is currently a great need for knowledge concerning management of old-age neurodegenerative diseases; the most important of which are: Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, and Huntington's disease. To summarize the potential of computationally predicted molecules and targets against neurodegenerative diseases. Review of literature published since 1997 against neurodegenerative diseases, utilizing as keywords: in silico, Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis ALS, and Huntington's disease. Due to the costs associated with experimentation and current ethical law, performing experiments directly on living organisms has become much more difficult. In this scenario, in silico techniques have been successful and have become powerful tools in the search to cure disease. Researchers use the Computer Aided Drug Design pipeline which: 1) generates 3-dimensional structures of target proteins through homology modeling 2) achieves stabilization through molecular dynamics simulation, and 3) exploits molecular docking through large compound libraries. Next generation sequencing is continually producing enormous amounts of raw sequence data while neuroimaging is producing a multitude of raw image data. To solve such pressing problems, these new tools and algorithms are required. This review elaborates precise in silico tools and techniques for drug targets, active molecules, and molecular docking studies, together with future prospects and challenges concerning possible breakthroughs in Alzheimer's, Parkinson's, Amyotrophic Lateral Sclerosis, and Huntington's disease. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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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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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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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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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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Directory of Open Access Journals (Sweden)
Xinyan Wang
Full Text Available Chronic obstructive pulmonary disease (COPD is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.
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Hellerstein, Marc K
2008-01-01
Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a naïve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.
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Novel Methods for Drug-Target Interaction Prediction using Graph Mining
Ba Alawi, Wail
2016-08-31
The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction
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Peptide-based proteasome inhibitors in anticancer drug design.
Micale, Nicola; Scarbaci, Kety; Troiano, Valeria; Ettari, Roberta; Grasso, Silvana; Zappalà, Maria
2014-09-01
The identification of the key role of the eukaryotic 26S proteasome in regulated intracellular proteolysis and its importance as a target in many pathological conditions wherein the proteasomal activity is defective (e.g., malignancies, autoimmune diseases, neurodegenerative diseases, etc.) prompted several research groups to the development of specific inhibitors of this multicatalytic complex with the aim of obtaining valid drug candidates. In regard to the anticancer therapy, the peptide boronate bortezomib (Velcade®) represents the first molecule approved by FDA for the treatment of multiple myeloma in 2003 and mantle cell lymphoma in 2006. Since then, a plethora of molecules targeting the proteasome have been identified as potential anticancer agents and a few of them reached clinical trials or are already in the market (i.e., carfilzomib; Kyprolis®). In most cases, the design of new proteasome inhibitors (PIs) takes into account a proven peptide or pseudopeptide motif as a base structure and places other chemical entities throughout the peptide skeleton in such a way to create an efficacious network of interactions within the catalytic sites. The purpose of this review is to provide an in-depth look at the current state of the research in the field of peptide-based PIs, specifically those ones that might find an application as anticancer agents. © 2014 Wiley Periodicals, Inc.
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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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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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Generative Recurrent Networks for De Novo Drug Design.
Gupta, Anvita; Müller, Alex T; Huisman, Berend J H; Fuchs, Jens A; Schneider, Petra; Schneider, Gisbert
2018-01-01
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
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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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Khambhla, Ekta; Shah, Viral; Baviskar, Kalpesh
2016-01-01
The current epoch has witnessed a lifestyle impregnated with stress, which is a major cause of several neurological disorders. High morbidity and mortality rate due to neurological diseases and disorders have generated a huge social impact. Despite voluminous research, patients suffering from fatal and/or debilitating CNS diseases such as brain tumors, HIV, encephalopathy, Alzheimer's, epilepsy, Parkinson's, migraine and multiple sclerosis outnumbered those suffering from systemic cancer or heart diseases. The brain being a highly sensitive neuronal organ, has evolved with vasculature barriers, which regulates the efflux and influx of substances to CNS. Treatment of CNS diseases/disorders is challenging because of physiologic, metabolic and biochemical obstacles created by these barriers which comprise mainly of BBB and BCFB. The inability of achieving therapeutically active concentration has become the bottleneck level difficulty, hampering the therapeutic efficiency of several promising drug candidates for CNS related disorders. Parallel maturation of an effective CNS drug delivery strategy with CNS drug discovery is the need of the hour. Recently, the focus of the pharmaceutical community has aggravated in the direction of developing novel and more efficient drug delivery systems, giving the potential of more effective and safer CNS therapies. The present review outlines several hurdles in drug delivery to the CNS along with ideal physicochemical properties desired in drug substance/formulation for CNS delivery. The review also focuses on different conventional and novel strategies for drug delivery to the CNS. The article also assesses and emphasizes on possible benefits of biomaterial based formulations for drug delivery to the CNS.
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Directory of Open Access Journals (Sweden)
Fan Ding
Full Text Available As a complex system, the complicated interactions between chemical ingredients, as well as the potential rules of interactive associations among chemical ingredients of traditional Chinese herbal formulae are not yet fully understood by modern science. On the other hand, network analysis is emerging as a powerful approach focusing on processing complex interactive data. By employing network approach in selected Chinese herbal formulae for the treatment of coronary heart disease (CHD, this article aims to construct and analyze chemical ingredients network of herbal formulae, and provide candidate herbs, chemical constituents, and ingredient groups for further investigation. As a result, chemical ingredients network composed of 1588 ingredients from 36 herbs used in 8 core formulae for the treatment of CHD was produced based on combination associations in herbal formulae. In this network, 9 communities with relative dense internal connections are significantly associated with 14 kinds of chemical structures with P<0.001. Moreover, chemical structural fingerprints of network communities were detected, while specific centralities of chemical ingredients indicating different levels of importance in the network were also measured. Finally, several distinct herbs, chemical ingredients, and ingredient groups with essential position in the network or high centrality value are recommended for further pharmacology study in the context of new drug development.
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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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Directory of Open Access Journals (Sweden)
Eduardo Pérez-Palma
Full Text Available Genome-wide association studies (GWAS have successfully identified several risk loci for Alzheimer's disease (AD. Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls derived from three publicly available GWAS in AD and replicated a broad genomic region (>248,000 bp associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting gene-wise p-values (GW, defined as the single strongest association signal within a gene, or calculated a more stringent gene-based association p-value using the extended Simes (GATES procedure. Comparison of these strategies revealed that ontological sub-networks (SNs involved in glutamate signaling were significantly overrepresented in AD (p<2.7×10(-11, p<1.9×10(-11; GW and GATES, respectively. Notably, glutamate signaling SNs were also found to be significantly overrepresented (p<5.1×10(-8 in the Alzheimer's disease Neuroimaging Initiative (ADNI study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder.
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Alshahrani, Mona
2018-04-30
In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease\\'s (or patient\\'s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results: We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.
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DEFF Research Database (Denmark)
Johansen, Morten Bo; Gonzalez-Izarzugaza, Jose Maria; Brunak, Søren
2013-01-01
We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features...
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A study of EMR-based medical knowledge network and its applications.
Zhao, Chao; Jiang, Jingchi; Xu, Zhiming; Guan, Yi
2017-05-01
Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support. We attempt to integrate this medical knowledge into a complex network, and then implement a diagnosis model based on this network. The dataset of our study contains 992 records which are uniformly sampled from different departments of the hospital. In order to integrate the knowledge of these records, an EMR-based medical knowledge network (EMKN) is constructed. This network takes medical entities as nodes, and co-occurrence relationships between the two entities as edges. Selected properties of this network are analyzed. To make use of this network, a basic diagnosis model is implemented. Seven hundred records are randomly selected to re-construct the network, and the remaining 292 records are used as test records. The vector space model is applied to illustrate the relationships between diseases and symptoms. Because there may exist more than one actual disease in a record, the recall rate of the first ten results, and the average precision are adopted as evaluation measures. Compared with a random network of the same size, this network has a similar average length but a much higher clustering coefficient. Additionally, it can be observed that there are direct correlations between the community structure and the real department classes in the hospital. For the diagnosis model, the vector space model using disease as a base obtains the best result. At least one accurate disease can be obtained in 73.27% of the records in the first ten results. We constructed an EMR-based medical knowledge network by extracting the medical entities. This network has the small-world and scale-free properties. Moreover, the community structure showed that entities in the same department have a tendency to be self-aggregated. Based on this network, a diagnosis model was proposed. This model uses only the symptoms as inputs and is not restricted to a specific
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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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Li, Cheng-Wei; Chen, Bor-Sen
2016-01-01
Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.
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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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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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Pillai, Satish K; Beekmann, Susan E; Santibanez, Scott; Polgreen, Philip M
2014-04-01
In 1995, the Centers for Disease Control and Prevention granted a Cooperative Agreement Program award to the Infectious Diseases Society of America to develop a provider-based emerging infections sentinel network, the Emerging Infections Network (EIN). Over the past 17 years, the EIN has evolved into a flexible, nationwide network with membership representing a broad cross-section of infectious disease physicians. The EIN has an active electronic mail conference (listserv) that facilitates communication among infectious disease providers and the public health community, and also sends members periodic queries (short surveys on infectious disease topics) that have addressed numerous topics relevant to both clinical infectious diseases and public health practice. The article reviews how the various functions of EIN contribute to clinical care and public health, identifies opportunities to further link clinical medicine and public health, and describes future directions for the EIN.
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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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A Biologically-Based Computational Approach to Drug Repurposing for Anthrax Infection
Directory of Open Access Journals (Sweden)
Jane P. F. Bai
2017-03-01
Full Text Available Developing drugs to treat the toxic effects of lethal toxin (LT and edema toxin (ET produced by B. anthracis is of global interest. We utilized a computational approach to score 474 drugs/compounds for their ability to reverse the toxic effects of anthrax toxins. For each toxin or drug/compound, we constructed an activity network by using its differentially expressed genes, molecular targets, and protein interactions. Gene expression profiles of drugs were obtained from the Connectivity Map and those of anthrax toxins in human alveolar macrophages were obtained from the Gene Expression Omnibus. Drug rankings were based on the ability of a drug/compound’s mode of action in the form of a signaling network to reverse the effects of anthrax toxins; literature reports were used to verify the top 10 and bottom 10 drugs/compounds identified. Simvastatin and bepridil with reported in vitro potency for protecting cells from LT and ET toxicities were computationally ranked fourth and eighth. The other top 10 drugs were fenofibrate, dihydroergotamine, cotinine, amantadine, mephenytoin, sotalol, ifosfamide, and mefloquine; literature mining revealed their potential protective effects from LT and ET toxicities. These drugs are worthy of investigation for their therapeutic benefits and might be used in combination with antibiotics for treating B. anthracis infection.
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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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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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Directory of Open Access Journals (Sweden)
Delong Zhang
Full Text Available Many studies have demonstrated that the pathophysiology and clinical symptoms of Parkinson's disease (PD are inhomogeneous. However, the symptom-specific intrinsic neural activities underlying the PD subtypes are still not well understood. Here, 15 tremor-dominant PD patients, 10 non-tremor-dominant PD patients, and 20 matched normal controls (NCs were recruited and underwent resting-state functional magnetic resonance imaging (fMRI. Functional brain networks were constructed based on randomly generated anatomical templates with and without the cerebellum. The regional network efficiencies (i.e., the local and global efficiencies were further measured and used to distinguish subgroups of PD patients (i.e., with tremor-dominant PD and non-tremor-dominant PD from the NCs using linear discriminant analysis. The results demonstrate that the subtype-specific functional networks were small-world-organized and that the network regional efficiency could discriminate among the individual PD subgroups and the NCs. Brain regions involved in distinguishing between the study groups included the basal ganglia (i.e., the caudate and putamen, limbic regions (i.e., the hippocampus and thalamus, the cerebellum, and other cerebral regions (e.g., the insula, cingulum, and calcarine sulcus. In particular, the performances of the regional local efficiency in the functional network were better than those of the global efficiency, and the performances of global efficiency were dependent on the inclusion of the cerebellum in the analysis. These findings provide new evidence for the neurological basis of differences between PD subtypes and suggest that the cerebellum may play different roles in the pathologies of different PD subtypes. The present study demonstrated the power of the combination of graph-based network analysis and discrimination analysis in elucidating the neural basis of different PD subtypes.
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van Roon, Eric N.; van den Bemt, Patricia M. L. A.; Jansen, Tim L. Th. A.; Houtman, Nella M.; van de Laar, Mart A. F. J.; Brouwers, Jacobus R. B. J.
Background: Clinically relevant drug-drug interactions (DDIs) must be recognized in a timely manner and managed appropriately to prevent adverse drug reactions or therapeutic failure. Because the evidence for most DDIs is based on case reports or poorly documented clinical information, there is a
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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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Directory of Open Access Journals (Sweden)
Nolan Priedigkeit
2015-02-01
Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.
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AlShahrani, Mona; Hoehndorf, Robert
2018-01-01
In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease's (or patient's) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results: We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.
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SemaTyP: a knowledge graph based literature mining method for drug discovery.
Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian
2018-05-30
Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.
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Directory of Open Access Journals (Sweden)
Chao Huang
Full Text Available Veterinary Herbal Medicine (VHM is a comprehensive, current, and informative discipline on the utilization of herbs in veterinary practice. Driven by chemistry but progressively directed by pharmacology and the clinical sciences, drug research has contributed more to address the needs for innovative veterinary medicine for curing animal diseases. However, research into veterinary medicine of vegetal origin in the pharmaceutical industry has reduced, owing to questions such as the short of compatibility of traditional natural-product extract libraries with high-throughput screening. Here, we present a cross-species chemogenomic screening platform to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future veterinary medicines, thereby increasing the number of treatment options. First, based on critically examined pharmacology and text mining, we build a cross-species drug-likeness evaluation approach to screen the lead compounds in veterinary medicines. Second, a specific cross-species target prediction model is developed to infer drug-target connections, with the purpose of understanding how drugs work on the specific targets. Third, we focus on exploring the multiple targets interference effects of veterinary medicines by heterogeneous network convergence and modularization analysis. Finally, we manually integrate a disease pathway to test whether the cross-species chemogenomic platform could uncover the active mechanism of veterinary medicine, which is exemplified by a specific network module. We believe the proposed cross-species chemogenomic platform allows for the systematization of current and traditional knowledge of veterinary medicine and, importantly, for the application of this emerging body of knowledge to the development of new drugs for animal diseases.
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Budget impact analysis of drugs for ultra-orphan non-oncological diseases in Europe.
Schlander, Michael; Adarkwah, Charles Christian; Gandjour, Afschin
2015-02-01
Ultra-orphan diseases (UODs) have been defined by a prevalence of less than 1 per 50,000 persons. However, little is known about budget impact of ultra-orphan drugs. For analysis, the budget impact analysis (BIA) had a time horizon of 10 years (2012-2021) and a pan-European payer's perspective, based on prevalence data for UODs for which patented drugs are available and/or for which drugs are in clinical development. A total of 18 drugs under patent protection or orphan drug designation for non-oncological UODs were identified. Furthermore, 29 ultra-orphan drugs for non-oncological diseases under development that have the potential of reaching the market by 2021 were found. Total budget impact over 10 years was estimated to be €15,660 and €4965 million for approved and pipeline ultra-orphan drugs, respectively (total: €20,625 million). The analysis does not support concerns regarding an uncontrolled growth in expenditures for drugs for UODs.
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Pläschke, Rachel N; Cieslik, Edna C; Müller, Veronika I; Hoffstaedter, Felix; Plachti, Anna; Varikuti, Deepthi P; Goosses, Mareike; Latz, Anne; Caspers, Svenja; Jockwitz, Christiane; Moebus, Susanne; Gruber, Oliver; Eickhoff, Claudia R; Reetz, Kathrin; Heller, Julia; Südmeyer, Martin; Mathys, Christian; Caspers, Julian; Grefkes, Christian; Kalenscher, Tobias; Langner, Robert; Eickhoff, Simon B
2017-12-01
Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc. © 2017
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Functional networks inference from rule-based machine learning models.
Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume
2016-01-01
Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The
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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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The MicroRNA Interaction Network of Lipid Diseases
Kandhro, Abdul H.; Shoombuatong, Watshara; Nantasenamat, Chanin; Prachayasittikul, Virapong; Nuchnoi, Pornlada
2017-01-01
Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes. Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes. Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms. Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets. PMID:29018475
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Drug repurposing by integrated literature mining and drug–gene–disease triangulation
DEFF Research Database (Denmark)
Sun, Peng; Guo, Jiong; Winnenburg, Rainer
2017-01-01
recent developments in computational drug repositioning and introduce the utilized data sources. Afterwards, we introduce a new data fusion model based on n-cluster editing as a novel multi-source triangulation strategy, which was further combined with semantic literature mining. Our evaluation suggests...... that utilizing drug–gene–disease triangulation coupled to sophisticated text analysis is a robust approach for identifying new drug candidates for repurposing....
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[Cooperative Cardiovascular Disease Research Network (RECAVA)].
García-Dorado, David; Castro-Beiras, Alfonso; Díez, Javier; Gabriel, Rafael; Gimeno-Blanes, Juan R; Ortiz de Landázuri, Manuel; Sánchez, Pedro L; Fernández-Avilés, Francisco
2008-01-01
Today, cardiovascular disease is the principal cause of death and hospitalization in Spain, and accounts for an annual healthcare budget of more than 4000 million euros. Consequently, early diagnosis, effective prevention, and the optimum treatment of cardiovascular disease present a significant social and healthcare challenge for the country. In this context, combining all available resources to increase the efficacy and healthcare benefits of scientific research is a priority. This rationale prompted the establishment of the Spanish Cooperative Cardiovascular Disease Research Network, or RECAVA (Red Temática de Investigación Cooperativa en Enfermedades Cardiovasculares), 5 years ago. Since its foundation, RECAVA's activities have focused on achieving four objectives: a) to facilitate contacts between basic, clinical and epidemiological researchers; b) to promote the shared use of advanced technological facilities; c) to apply research results to clinical practice, and d) to train a new generation of translational cardiovascular researchers in Spain. At present, RECAVA consists of 41 research groups and seven shared technological facilities. RECAVA's research strategy is based on a scientific design matrix centered on the most important cardiovascular processes. The level of RECAVA's research activity is reflected in the fact that 28 co-authored articles were published in international journals during the first six months of 2007, with each involving contributions from at least two groups in the network. Finally, RECAVA also participates in the work of the Spanish National Center for Cardiovascular Research, or CNIC (Centro Nacional de Investigación Cardiovascular), and some established Biomedical Research Network Centers, or CIBER (Centros de Investigación Biomédica en RED), with the aim of consolidating the development of a dynamic multidisciplinary research framework that is capable of meeting the growing challenge that cardiovascular disease will present
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Ding, Fangrui; Tan, Aidi; Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie
2016-01-01
Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet
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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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Kurti, Zsuzsanna; Vegh, Zsuzsanna; Golovics, Petra A; Fadgyas-Freyler, Petra; Gecse, Krisztina B; Gonczi, Lorant; Gimesi-Orszagh, Judit; Lovasz, Barbara D; Lakatos, Peter L
2016-11-01
Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammatory diseases associated with a substantial healthcare utilization. Our aim was to estimate the national prevalence of inflammatory bowel disease (IBD), CD and UC and to describe current drug treatment practices in CD and UC. Patients and drug dispensing events were identified according to international classification codes for UC and CD in in-patient care, non-primary out-patient care and drug prescription databases (2011-2013) of the National Health Insurance Fund. A total of 55,039 individuals (men: 44.6%) with physician-diagnosed IBD were alive in Hungary in 2013, corresponding to a prevalence of 0.55% (95% CI, 0.55-0.56). The prevalence of CD 0.20% (95% CI, 0.19-0.20), and UC was 0.34% (95% CI, 0.33-0.34). The prevalence both in men and women was the highest in the 20-39 year-olds in CD. Current use of immunosuppressives and biological therapy was highest in the pediatric CD population (44% and 15%) followed by adult CD (33% and 9%), while their use was lowest in elderly patients. Interestingly, current use of 5-ASA (5-aminosalicylates) was high in both UC and CD irrespective of the age group. The Hungarian IBD prevalence based on nationwide database of the National Health Insurance Fund was high. We identified significant differences in the drug prescription practices according to age-groups. Copyright © 2016 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
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Directory of Open Access Journals (Sweden)
Abby E Rudolph
2011-05-01
Full Text Available Respondent driven sampling (RDS was originally developed to sample and provide peer education to injection drug users at risk for HIV. Based on the premise that drug users' social networks were maintained through sharing rituals, this peer-driven approach to disseminate educational information and reduce risk behaviors capitalizes and expands upon the norms that sustain these relationships. Compared with traditional outreach interventions, peer-driven interventions produce greater reductions in HIV risk behaviors and adoption of safer behaviors over time, however, control and intervention groups are not similarly recruited. As peer-recruitment may alter risk networks and individual risk behaviors over time, such comparison studies are unable to isolate the effect of a peer-delivered intervention. This analysis examines whether RDS recruitment (without an intervention is associated with changes in health-seeking behaviors and network composition over 6 months. New York City drug users (N = 618 were recruited using targeted street outreach (TSO and RDS (2006-2009. 329 non-injectors (RDS = 237; TSO = 92 completed baseline and 6-month surveys ascertaining demographic, drug use, and network characteristics. Chi-square and t-tests compared RDS- and TSO-recruited participants on changes in HIV testing and drug treatment utilization and in the proportion of drug using, sex, incarcerated and social support networks over the follow-up period. The sample was 66% male, 24% Hispanic, 69% black, 62% homeless, and the median age was 35. At baseline, the median network size was 3, 86% used crack, 70% used cocaine, 40% used heroin, and in the past 6 months 72% were tested for HIV and 46% were enrolled in drug treatment. There were no significant differences by recruitment strategy with respect to changes in health-seeking behaviors or network composition over 6 months. These findings suggest no association between RDS recruitment and changes in
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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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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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Drug target ontology to classify and integrate drug discovery data.
Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C
2017-11-09
One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic
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de Sousa, Francisco Fabio Oliveira; Ferraz, Camila; Rodrigues, Lidiany K Arla de Azevedo; Nojosa, Jacqueline de Santiago; Yamauti, Monica
2014-01-01
Dental disorders, such as caries, periodontal and endodontic diseases are major public health issues worldwide. In common, they are biofilm-dependent oral diseases, and the specific conditions of oral cavity may develop infectious foci that could affect other physiological systems. Efforts have been made to develop new treatment routes for the treatment of oral diseases, and therefore, for the prevention of some systemic illnesses. New drugs and materials have been challenged to prevent and treat these conditions, especially by means of bacteria elimination. "Recent progresses in understanding the etiology, epidemiology and microbiology of the microbial flora in those circumstances have given insight and motivated the innovation on new therapeutic approaches for the management of the oral diseases progression". Some of the greatest advances in the medical field have been based in nanosized systems, ranging from the drug release with designed nanoparticles to tissue scaffolds based on nanotechnology. These systems offer new possibilities for specific and efficient therapies, been assayed successfully in preventive/curative therapies to the oral cavity, opening new challenges and opportunities to overcome common diseases based on bacterial biofilm development. The aim of this review is to summarize the recent nanotechnological developments in the drug delivery field related to the prevention and treatment of the major biofilm-dependent oral diseases and to identify those systems, which may have higher potential for clinical use.
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A Network Flow-based Analysis of Cognitive Reserve in Normal Ageing and Alzheimer's Disease.
Wook Yoo, Sang; Han, Cheol E; Shin, Joseph S; Won Seo, Sang; Na, Duk L; Kaiser, Marcus; Jeong, Yong; Seong, Joon-Kyung
2015-05-20
Cognitive reserve is the ability to sustain cognitive function even with a certain amount of brain damages. Here we investigate the neural compensation mechanism of cognitive reserve from the perspective of structural brain connectivity. Our goal was to show that normal people with high education levels (i.e., cognitive reserve) maintain abundant pathways connecting any two brain regions, providing better compensation or resilience after brain damage. Accordingly, patients with high education levels show more deterioration in structural brain connectivity than those with low education levels before symptoms of Alzheimer's disease (AD) become apparent. To test this hypothesis, we use network flow measuring the number of alternative paths between two brain regions in the brain network. The experimental results show that for normal aging, education strengthens network reliability, as measured through flow values, in a subnetwork centered at the supramarginal gyrus. For AD, a subnetwork centered at the left middle frontal gyrus shows a negative correlation between flow and education, which implies more collapse in structural brain connectivity for highly educated patients. We conclude that cognitive reserve may come from the ability of network reorganization to secure the information flow within the brain network, therefore making it more resistant to disease progress.
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Directory of Open Access Journals (Sweden)
Chen Xin
2012-10-01
Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable
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A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases
Directory of Open Access Journals (Sweden)
Zhiyong ZHANG
2014-03-01
Full Text Available The neural networks have significance on recognition of crops disease diagnosis? but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in the image recognition. At last, an example of the emulation shows that neural network model based on recognizes significantly better than without optimization. Model accuracy has been improved to a certain extent to meet the actual needs of maize leaf diseases recognition.
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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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Drug-perturbation-based stratification of blood cancer
Dietrich, Sascha; Lu, Junyan; Wu, Bian; Hüllein, Jennifer; da Silva Liberio, Michelle; Walther, Tatjana; Wagner, Lena; Rabe, Sophie; Ghidelli-Disse, Sonja; Bantscheff, Marcus; Słabicki, Mikołaj; Mock, Andreas; Oakes, Christopher C.; Wang, Shihui; Oppermann, Sina; Lukas, Marina; Kim, Vladislav; Sill, Martin; Jauch, Anna; Sutton, Lesley Ann; Rosenquist, Richard; Liu, Xiyang; Jethwa, Alexander; Lee, Kwang Seok; Lewis, Joe; Putzker, Kerstin; Lutz, Christoph; Rossi, Davide; Oellerich, Thomas; Herling, Marco; Nguyen-Khac, Florence; Plass, Christoph; von Kalle, Christof; Ho, Anthony D.; Hensel, Manfred; Dürig, Jan; Ringshausen, Ingo; Huber, Wolfgang
2017-01-01
As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non–BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care. PMID:29227286
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Wang, H; Gu, Q; Wei, J; Cao, Z; Liu, Q
2015-05-01
A novel recommendation-based drug repositioning strategy is presented to simultaneously determine novel drug indications and side effects in one integrated framework. This strategy provides a complementary method to medical genetics-based drug repositioning, which reduces the occurrence of false positives in medical genetics-based drug repositioning, resulting in a ranked list of new candidate indications and/or side effects with different confidence levels. Several new drug indications and side effects are reported with high prediction confidences. © 2015 American Society for Clinical Pharmacology and Therapeutics.
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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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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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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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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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Wang, Xiaojie; Zheng, Hong; Shou, Tao; Tang, Chunming; Miao, Kun; Wang, Ping
2017-03-29
Osteosarcoma is the most common malignant bone tumour. Due to the high metastasis rate and drug resistance of this disease, multi-drug regimens are necessary to control tumour cells at various stages of the cell cycle, eliminate local or distant micrometastases, and reduce the emergence of drug-resistant cells. Many adjuvant chemotherapy protocols have shown different efficacies and controversial results. Therefore, we classified the types of drugs used for adjuvant chemotherapy and evaluated the differences between single- and multi-drug chemotherapy regimens using network meta-analysis. We searched electronic databases, including PubMed (MEDLINE), EmBase, and the Cochrane Library, through November 2016 using the keywords "osteosarcoma", "osteogenic sarcoma", "chemotherapy", and "random*" without language restrictions. The major outcome in the present analysis was progression-free survival (PFS), and the secondary outcome was overall survival (OS). We used a random effect network meta-analysis for mixed multiple treatment comparisons. We included 23 articles assessing a total of 5742 patients in the present systematic review. The analysis of PFS indicated that the T12 protocol (including adriamycin, bleomycin, cyclophosphamide, dactinomycin, methotrexate, cisplatin) plays a more critical role in osteosarcoma treatment (surface under the cumulative ranking (SUCRA) probability 76.9%), with a better effect on prolonging the PFS of patients when combined with ifosfamide (94.1%) or vincristine (81.9%). For the analysis of OS, we separated the regimens to two groups, reflecting the disconnection. The T12 protocol plus vincristine (94.7%) or the removal of cisplatinum (89.4%) is most likely the best regimen. We concluded that multi-drug regimens have a better effect on prolonging the PFS and OS of osteosarcoma patients, and the T12 protocol has a better effect on prolonging the PFS of osteosarcoma patients, particularly in combination with ifosfamide or vincristine
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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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Neuronal network disintegration: common pathways linking neurodegenerative diseases.
Ahmed, Rebekah M; Devenney, Emma M; Irish, Muireann; Ittner, Arne; Naismith, Sharon; Ittner, Lars M; Rohrer, Jonathan D; Halliday, Glenda M; Eisen, Andrew; Hodges, John R; Kiernan, Matthew C
2016-11-01
Neurodegeneration refers to a heterogeneous group of brain disorders that progressively evolve. It has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels and therefore traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Neuronal network disintegration is fundamental to neurodegeneration, and concepts based around such a concept may better explain the overlap between their clinical and pathological phenotypes. In this Review, promoters of overlap in neurodegeneration incorporating behavioural, cognitive, metabolic, motor, and extrapyramidal presentations will be critically appraised. In addition, evidence that may support the existence of large-scale networks that might be contributing to phenotypic differentiation will be considered across a neurodegenerative spectrum. Disintegration of neuronal networks through different pathological processes, such as prion-like spread, may provide a better paradigm of disease and thereby facilitate the identification of novel therapies for neurodegeneration. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
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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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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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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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Dombrowski, Kirk; Khan, Bilal; Habecker, Patrick; Hagan, Holly; Friedman, Samuel R; Saad, Mohamed
2017-04-01
This article explores how social network dynamics may have reduced the spread of HIV-1 infection among people who inject drugs during the early years of the epidemic. Stochastic, discrete event, agent-based simulations are used to test whether a "firewall effect" can arise out of self-organizing processes at the actor level, and whether such an effect can account for stable HIV prevalence rates below population saturation. Repeated simulation experiments show that, in the presence of recurring, acute, and highly infectious outbreaks, micro-network structures combine with the HIV virus's natural history to reduce the spread of the disease. These results indicate that network factors likely played a significant role in the prevention of HIV infection within injection risk networks during periods of peak prevalence. They also suggest that social forces that disturb network connections may diminish the natural firewall effect and result in higher rates of HIV.
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[Guideline for the treatment of Graves' disease with antithyroid drug].
Nakamura, Hirotoshi
2006-12-01
We have published "Guideline for the Treatment of Graves' Disease with Antithyroid Drug in Japan 2006" in the middle of May from the Japan Thyroid Association. The background, working process, composition, aim and significance of this guideline are described. The most remarkable feature of this guideline is "evidence based".
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Disease-responsive drug delivery: the next generation of smart delivery devices.
Wanakule, Prinda; Roy, Krishnendu
2012-01-01
With the advent of highly potent and cytotoxic drugs, it is increasingly critical that they be targeted and released only in cells of diseased tissues, while sparing physiologically normal neighbors. Simple ligand-based targeting of drug carriers, although promising, cannot always provide the required specificity to achieve this since often normal cells also express significant levels of the targeted receptors. Therefore, stimuli-responsive delivery systems are being explored to allow drug release from nano- and microcarriers and implantable devices, primarily in the presence of physiological or disease-specific pathophysiological signals. Designing smart biomaterials that respond to temperature or pH changes, protein and ligand binding, disease-specific degradation, e.g. enzymatic cleavage, has become an integral part of this approach. These strategies are used in combination with nano- and microparticle systems to improve delivery efficiency through several routes of administration, and with injectable or implantable systems for long term controlled release. This review focuses on recent developments in stimuli-responsive systems, their physicochemical properties, release profiles, efficacy, safety and biocompatibility, as well as future perspectives.
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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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Ceramic/polymer nanocomposites with tunable drug delivery capability at specific disease sites.
Liu, Huinan; Webster, Thomas J
2010-06-01
Pharmaceutical agents are often used to stimulate new bone formation for the treatment of bone injuries or diseases (such as osteoporosis). However, there are several problems associated with current orthopedic drug delivery methods. First, conventional systemic administration of pharmaceutical agents may not effectively reach targeted sites and, thus, they can cause nonspecific bone formation in areas not affected by injury or disease. Second, even if intentionally delivered or implanted locally to the damaged bone tissue, these agents tend to rapidly diffuse into adjacent tissues due to weak physical bonding to their drug carriers, which limits their potential to promote prolonged bone formation in targeted areas of bone disease. Therefore, in this study, biodegradable ceramic/polymer nanocomposites were explored as novel drug carriers for orthopedic applications to prolong local drug release and, thus, improve drug effectiveness at bone disease sites. Specifically, a bone morphogenetic protein (BMP-7) derived peptide (DIF-7c) was used as a model drug in this study and was first loaded onto nanocrystalline hydroxyapatite (nano-HA) by either covalent chemical attachment or physical adsorption. These drug-carrying nano-HA particles were then dispersed into a degradable polymer (poly-lactide-co-glycolide or PLGA) matrix to create an implantable system capable of long-term drug release. The aminophase silane covalent chemical immobilization process was utilized in this study. These nanocomposite-based drug delivery systems were then characterized for drug loading efficiency and in vitro drug release. Results demonstrated that DIF-7c was successfully immobilized onto nano-HA placed in PLGA. Moreover, a greater prolonged two-phase release profile (of more than 3 months) was achieved when using aminophase silane chemical immobilization to nano-HA particles. Since previous studies have demonstrated greater in vivo bone growth on nano- compared with micron-HA particles
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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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Deng, Chen-Hui; Zhang, Guan-Min; Bi, Shan-Shan; Zhou, Tian-Yan; Lu, Wei
2011-07-01
This study is to develop a therapeutic drug monitoring (TDM) network server of tacrolimus for Chinese renal transplant patients, which can facilitate doctor to manage patients' information and provide three levels of predictions. Database management system MySQL was employed to build and manage the database of patients and doctors' information, and hypertext mark-up language (HTML) and Java server pages (JSP) technology were employed to construct network server for database management. Based on the population pharmacokinetic model of tacrolimus for Chinese renal transplant patients, above program languages were used to construct the population prediction and subpopulation prediction modules. Based on Bayesian principle and maximization of the posterior probability function, an objective function was established, and minimized by an optimization algorithm to estimate patient's individual pharmacokinetic parameters. It is proved that the network server has the basic functions for database management and three levels of prediction to aid doctor to optimize the regimen of tacrolimus for Chinese renal transplant patients.
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Nagamani, S; Gaur, A S; Tanneeru, K; Muneeswaran, G; Madugula, S S; Consortium, Mpds; Druzhilovskiy, D; Poroikov, V V; Sastry, G N
2017-11-01
Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.
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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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Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin
2018-06-14
Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.
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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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Lee, Dong-Gi; Shin, Hyunjung
2017-05-18
Recently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an association. This representation results in the difficulty of identifying prior diseases and their influence on posterior diseases. In this paper, we propose a causal disease network that implements disease causality through text mining on biomedical literature. To identify the causality between diseases, the proposed method includes two schemes: the first is the lexicon-based causality term strength, which provides the causal strength on a variety of causality terms based on lexicon analysis. The second is the frequency-based causality strength, which determines the direction and strength of causality based on document and clause frequencies in the literature. We applied the proposed method to 6,617,833 PubMed literature, and chose 195 diseases to construct a causal disease network. From all possible pairs of disease nodes in the network, 1011 causal pairs of 149 diseases were extracted. The resulting network was compared with that of a previous study. In terms of both coverage and quality, the proposed method showed outperforming results; it determined 2.7 times more causalities and showed higher correlation with associated diseases than the existing method. This research has novelty in which the proposed method circumvents the limitations of time and cost in applying all possible causalities in biological experiments and it is a more advanced text mining technique by defining the concepts of causality term strength.
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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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Pectin-based colon-specific drug delivery
Shailendra Shukla; Deepak Jain; Kavita Verma; Shiddarth Verma
2011-01-01
Colon-specific drug delivery have a great importance in the delivery of drugs for the treatment of local colonic, as well as systemic diseases like Crohn′s disease, ulcerative colitis, colorectal cancer, amoebiasis, asthma, arthritis and inflammation which can be achieved by targeted delivery of drug to colon. Specific systemic absorption in the colon gave interesting possibilities for the delivery of protein and peptides. It contains relatively less proteolytic enzyme activities in the colon...
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Rhamnogalacturonan-I based microcapsules for targeted drug release
DEFF Research Database (Denmark)
Svagan, Anna J.; Kusic, Anja; De Gobba, Cristian
2016-01-01
Drug targeting to the colon via the oral administration route for local treatment of e.g. inflammatory bowel disease and colonic cancer has several advantages such as needle-free administration and low infection risk. A new source for delivery is plant-polysaccharide based delivery platforms...... such as Rhamnogalacturonan-I (RG-I). In the gastro-intestinal tract the RG-I is only degraded by the action of the colonic microflora. For assessment of potential drug delivery properties, RG-I based microcapsules (~1 μm in diameter) were prepared by an interfacial poly-addition reaction. The cross-linked capsules were...
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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
criteria for drug development, price levels and use needs to be readdressed to improve drug safety and minimise costs. New global health policies based on cheaper drugs can help the treatment of many categories of orphan and rare diseases and millions of orphan patients in developing and developed countries.
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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
criteria for drug development, price levels and use needs to be readdressed to improve drug safety and minimise costs. New global health policies based on cheaper drugs can help the treatment of many categories of orphan and rare diseases and millions of orphan patients in developing and developed countries. PMID:25332915
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Drug-induced interstitial lung diseases. Often forgotten
International Nuclear Information System (INIS)
Poschenrieder, F.; Stroszczynski, C.; Hamer, O.W.
2014-01-01
Drug-induced interstitial lung diseases (DILD) are probably more common than diagnosed. Due to their potential reversibility, increased vigilance towards DILD is appropriate also from the radiologist's point of view, particularly as these diseases regularly exhibit radiological correlates in high-resolution computed tomography (HRCT) of the lungs. Based on personal experience typical relatively common manifestations of DILD are diffuse alveolar damage (DAD), eosinophilic pneumonia (EP), hypersensitivity pneumonitis (HP), organizing pneumonia (OP), non-specific interstitial pneumonia (NSIP) and usual interstitial pneumonia (UIP). These patterns are presented based on case studies, whereby emphasis is placed on the clinical context. This is to highlight the relevance of interdisciplinary communication and discussion in the diagnostic field of DILD as it is a diagnosis of exclusion or of probability in most cases. Helpful differential diagnostic indications for the presence of DILD, such as an accompanying eosinophilia or increased attenuation of pulmonary consolidations in amiodarone-induced pneumopathy are mentioned and the freely available online database http://www.pneumotox.com is presented. (orig.) [de
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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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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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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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Neutrophil targeted nano-drug delivery system for chronic obstructive lung diseases.
Vij, Neeraj; Min, Taehong; Bodas, Manish; Gorde, Aakruti; Roy, Indrajit
2016-11-01
The success of drug delivery to target airway cell(s) remains a significant challenge due to the limited ability of nanoparticle (NP) systems to circumvent protective airway-defense mechanisms. The size, density, surface and physical-chemical properties of nanoparticles are the key features that determine their ability to navigate across the airway-barrier. We evaluated here the efficacy of a PEGylated immuno-conjugated PLGA-nanoparticle (PINP) to overcome this challenge and selectively deliver drug to specific inflammatory cells (neutrophils). We first characterized the size, shape, surface-properties and neutrophil targeting using dynamic laser scattering, transmission electron microscopy and flow cytometry. Next, we assessed the efficacy of neutrophil-targeted PINPs in transporting through the airway followed by specific binding and release of drug to neutrophils. Finally, our results demonstrate the efficacy of PINP mediated non-steroidal anti-inflammatory drug-(ibuprofen) delivery to neutrophils in murine models of obstructive lung diseases, based on its ability to control neutrophilic-inflammation and resulting lung disease. Copyright © 2016 Elsevier Inc. All rights reserved.
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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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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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Hemispheric asymmetry of electroencephalography-based functional brain networks.
Jalili, Mahdi
2014-11-12
Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.
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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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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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Computational prediction of drug-drug interactions based on drugs functional similarities.
Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah
2017-06-01
Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.
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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 o...
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Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening
2006-01-01
In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.
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Study on pattern recognition of Raman spectrum based on fuzzy neural network
Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing
2017-10-01
Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.
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Network-level reproduction number and extinction threshold for vector-borne diseases.
Xue, Ling; Scoglio, Caterina
2015-06-01
The basic reproduction number of deterministic models is an essential quantity to predict whether an epidemic will spread or not. Thresholds for disease extinction contribute crucial knowledge of disease control, elimination, and mitigation of infectious diseases. Relationships between basic reproduction numbers of two deterministic network-based ordinary differential equation vector-host models, and extinction thresholds of corresponding stochastic continuous-time Markov chain models are derived under some assumptions. Numerical simulation results for malaria and Rift Valley fever transmission on heterogeneous networks are in agreement with analytical results without any assumptions, reinforcing that the relationships may always exist and proposing a mathematical problem for proving existence of the relationships in general. Moreover, numerical simulations show that the basic reproduction number does not monotonically increase or decrease with the extinction threshold. Consistent trends of extinction probability observed through numerical simulations provide novel insights into mitigation strategies to increase the disease extinction probability. Research findings may improve understandings of thresholds for disease persistence in order to control vector-borne diseases.
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A Highly Infectious Disease Care Network in the US Healthcare System.
Le, Aurora B; Biddinger, Paul D; Smith, Philip W; Herstein, Jocelyn J; Levy, Deborah A; Gibbs, Shawn G; Lowe, John J
During the 2014-15 Ebola outbreak in West Africa, the United States responded by stratifying hospitals into 1 of 3 Centers for Disease Control and Prevention (CDC)-designated categories-based on the hospital's ability to identify, isolate, assess, and provide care to patients with suspected or confirmed Ebola virus disease (EVD)-in an attempt to position the US healthcare system to safely isolate and care for potential patients. Now, with the Ebola epidemic quelled, it is crucial that we act on the lessons learned from the EVD response to broaden our national perspective on infectious disease mitigation and management, build on our newly enhanced healthcare capabilities to respond to infectious disease threats, develop a more cost-effective and sustainable model of infectious disease prevention, and continue to foster training so that the nation is not in a vulnerable position once more. We propose the formal creation of a US Highly Infectious Disease Care Network (HIDCN) modeled after 2 previous highly infectious disease consensus efforts in the United States and the European Union. A US Highly Infectious Disease Care Network can provide a common platform for the exchange of training, protocols, research, knowledge, and capability sharing among high-level isolation units. Furthermore, we envision the network will cultivate relationships among facilities and serve as a means of establishing national standards for infectious disease response, which will strengthen domestic preparedness and the nation's ability to respond to the next highly infectious disease threat.
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Nanotechnology and nanocarrier-based approaches on treatment of degenerative diseases
Chowdhury, Anindita; Kunjiappan, Selvaraj; Panneerselvam, Theivendren; Somasundaram, Balasubramanian; Bhattacharjee, Chiranjib
2017-04-01
Degenerative diseases are results of deterioration of cells and tissues with aging either by unhealthy lifestyle or normal senescence. The degenerative disease likely affects central nervous system and cardiovascular system to a great extent. Certain medications and therapies have emerged for the treatment of degenerative diseases, but in most cases bearing with poor solubility, lower bioavailability, drug resistance, and incapability to cross the blood-brain barrier (BBB). Hence, it has to be overcome with conventional treatment system; in this connection, nanotechnology has gained a great deal of interest in recent years. Moreover, nanotechnology and nanocarrier-based approach drug delivery system could revolutionize the treatment of degenerative diseases by faster absorption of drug, targeted interaction at specific site, and its release in a controlled manner into human body with minimal side effects. The core objective of this review is to customize and formulate therapeutically active molecules with specific site of action and without affecting other organs and tissues to obtain effective result in the improvement of quality of health. In addition, the review provides a concise insight into the recent developments and applications of nanotech and nanocarrier-based drug delivery for the treatment of various degenerative diseases.
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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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Evaluating drug trafficking on the Tor Network: Silk Road 2, the sequel.
Dolliver, Diana S
2015-11-01
Housing an illicit, online drug retail market generating sales in the millions of USD, the Silk Road was a profitable marketplace with a growing and loyal consumer base. Following its FBI-forced shut down in October 2013, the Silk Road enjoyed newfound fame that contributed to an increase in new users downloading and accessing the Tor Network; however, with this particular marketplace out of order, Silk Road 2 was launched to fill the void. The goals of this study were to (1) compare the metrics of Silk Road 2 to the original site, and to (2) determine if there were any indications of the presence of more sophisticated drug trafficking operations. Data were collected from Silk Road 2 during the months of August and September 2014 using webcrawling software. Silk Road 2 was a much smaller marketplace than the original Silk Road. Of the 1834 unique items for sale, 348 were drug items sold by 145 distinct vendors shipping from 19 countries. Of the drug items advertised, most were stimulants and hallucinogens. The United States is both the number one country of origin for drug sales on Silk Road 2 and the number one destination country. Interestingly, 73% of all vendor accounts on Silk Road 2 advertised drug items, even though drugs only constituted 19% of all items advertised. This study was the first to research Silk Road 2, the replacement illicit marketplace to the original virtual Silk Road. This study was also the first to examine indications of the presence of more coordinated drug trafficking efforts in an online setting. The findings indicated that while Silk Road 2 was not primarily a drug market, there were indications that some vendor accounts may have connections reaching beyond a base retail market. Copyright © 2015 Elsevier B.V. All rights reserved.
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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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Directory of Open Access Journals (Sweden)
Morten Bo Johansen
Full Text Available We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP.
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De, Prithwish; Cox, Joseph; Boivin, Jean-François; Platt, Robert W; Jolly, Ann M
2008-01-01
Secondary syringe exchange (SSE) refers to the exchange of sterile syringes between injection drug users (IDUs). To date there has been limited examination of SSE in relation to the social networks of IDUs. This study aimed to identify characteristics of drug injecting networks associated with the receipt of syringes through SSE. Active IDUs were recruited from syringe exchange and methadone treatment programs in Montreal, Canada, between April 2004 and January 2005. Information on each participant and on their drug-injecting networks was elicited using a structured, interviewer-administered questionnaire. Subjects' network characteristics were examined in relation to SSE using regression models with generalized estimating equations. Of 218 participants, 126 were SSE recipients with 186 IDUs in their injecting networks. The 92 non-recipients reported 188 network IDUs. Networks of SSE recipients and non-recipients were similar with regard to network size and demographics of network members. In multivariate analyses adjusted for age and gender, SSE recipients were more likely than non-recipients to self-report being HIV-positive (OR=3.56 [1.54-8.23]); require or provide help with injecting (OR=3.74 [2.01-6.95]); have a social network member who is a sexual partner (OR=1.90 [1.11-3.24]), who currently attends a syringe exchange or methadone program (OR=2.33 [1.16-4.70]), injects daily (OR=1.77 [1.11-2.84]), and shares syringes with the subject (OR=2.24 [1.13-4.46]). SSE is associated with several injection-related risk factors that could be used to help focus public health interventions for risk reduction. Since SSE offers an opportunity for the dissemination of important prevention messages, SSE-based networks should be used to improve public health interventions. This approach can optimize the benefits of SSE while minimizing the potential risks associated with the practice of secondary exchange.
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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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[Drug treatment of early-stage (de novo and "honeymoon") Parkinson disease].
Cesaro, P; Defebvre, L
2014-04-01
In this article, we discuss the management of motor symptoms during the early phases of Parkinson's disease, excluding that of any other clinical manifestation. We relied primarily upon recently published data and do not describe older publications relating to anticholinergic drugs or amantadine. The initial pharmacological treatment of idiopathic Parkinson's disease (IPD) is symptomatic and remains based upon dopaminergic drugs. However, the development of new drugs has broadened the range of strategic options and improved overall patient management. Announcing the diagnosis is a critical moment, as pointed out by patients' associations. Patients should be advised to maintain personal, professional, social and physical activities as long as possible. The potential benefit of early pharmacological treatment should be explained, focusing on the possible disease-modifying effect of drugs such as rasagiline. According to current guidelines, L-Dopa is preferred in patients above 65years of age, while those below 65 should be treated with dopamine agonists. Like monoamine oxidase inhibitors B (MAOI-B), synthetic dopamine agonists exhibit several advantages: easy-to-use treatment with a once-daily administration, delayed L-Dopa initiation, significant efficacy on motor symptoms (although lower than that of L-Dopa). MOAI can be prescribed in association with L-Dopa or dopamine agonists. Rasagiline also delays L-Dopa initiation, and consequently motor complications. Copyright © 2014 Elsevier Masson SAS. All rights reserved.
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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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Maes, Michael; Nowak, Gabriel; Caso, Javier R; Leza, Juan Carlos; Song, Cai; Kubera, Marta; Klein, Hans; Galecki, Piotr; Noto, Cristiano; Glaab, Enrico; Balling, Rudi; Berk, Michael
2016-07-01
Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular
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Altered resting state brain networks in Parkinson's disease.
Directory of Open Access Journals (Sweden)
Martin Göttlich
Full Text Available Parkinson's disease (PD is a neurodegenerative disorder affecting dopaminergic neurons in the substantia nigra leading to dysfunctional cortico-striato-thalamic-cortical loops. In addition to the characteristic motor symptoms, PD patients often show cognitive impairments, affective changes and other non-motor symptoms, suggesting system-wide effects on brain function. Here, we used functional magnetic resonance imaging and graph-theory based analysis methods to investigate altered whole-brain intrinsic functional connectivity in PD patients (n = 37 compared to healthy controls (n = 20. Global network properties indicated less efficient processing in PD. Analysis of brain network modules pointed to increased connectivity within the sensorimotor network, but decreased interaction of the visual network with other brain modules. We found lower connectivity mainly between the cuneus and the ventral caudate, medial orbitofrontal cortex and the temporal lobe. To identify regions of altered connectivity, we mapped the degree of intrinsic functional connectivity both on ROI- and on voxel-level across the brain. Compared to healthy controls, PD patients showed lower connectedness in the medial and middle orbitofrontal cortex. The degree of connectivity was also decreased in the occipital lobe (cuneus and calcarine, but increased in the superior parietal cortex, posterior cingulate gyrus, supramarginal gyrus and supplementary motor area. Our results on global network and module properties indicated that PD manifests as a disconnection syndrome. This was most apparent in the visual network module. The higher connectedness within the sensorimotor module in PD patients may be related to compensation mechanism in order to overcome the functional deficit of the striato-cortical motor loops or to loss of mutual inhibition between brain networks. Abnormal connectivity in the visual network may be related to adaptation and compensation processes as a consequence
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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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Plant-based Rasayana drugs from Ayurveda.
Balasubramani, Subramani Paranthaman; Venkatasubramanian, Padma; Kukkupuni, Subrahmanya Kumar; Patwardhan, Bhushan
2011-02-01
Rasayana tantra is one of the eight specialties of Ayurveda. It is a specialized practice in the form of rejuvenative recipes, dietary regimen, special health promoting behaviour and drugs. Properly administered Rasayana can bestow the human being with several benefits like longevity, memory, intelligence, freedom from diseases, youthful age, excellence of luster, complexion and voice, optimum strength of physique and sense organs, respectability and brilliance. Various types of plant based Rasayana recipes are mentioned in Ayurveda. Review of the current literature available on Rasayanas indicates that anti-oxidant and immunomodulation are the most studied activities of the Rasayana drugs. Querying in Pubmed database on Rasayanas reveals that single plants as well as poly herbal formulations have been researched on. This article reviews the basics of Rasayana therapy and the published research on different Rasayana drugs for specific health conditions. It also provides the possible directions for future research.
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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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Versatile Chemical Derivatizations to Design Glycol Chitosan-Based Drug Carriers
Directory of Open Access Journals (Sweden)
Sung Eun Kim
2017-10-01
Full Text Available Glycol chitosan (GC and its derivatives have been extensively investigated as safe and effective drug delivery carriers because of their unique physiochemical and biological properties. The reactive functional groups such as the amine and hydroxyl groups on the GC backbone allow for easy chemical modification with various chemical compounds (e.g., hydrophobic molecules, crosslinkers, and acid-sensitive and labile molecules, and the versatility in chemical modifications enables production of a wide range of GC-based drug carriers. This review summarizes the versatile chemical modification methods that can be used to design GC-based drug carriers and describes their recent applications in disease therapy.
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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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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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Atomoxetine restores the response inhibition network in Parkinson’s disease
Rae, Charlotte L.; Nombela, Cristina; Rodríguez, Patricia Vázquez; Ye, Zheng; Hughes, Laura E.; Jones, P. Simon; Ham, Timothy; Rittman, Timothy; Coyle-Gilchrist, Ian; Regenthal, Ralf; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.
2016-01-01
Abstract Parkinson’s disease impairs the inhibition of responses, and whilst impulsivity is mild for some patients, severe impulse control disorders affect ∼10% of cases. Based on preclinical models we proposed that noradrenergic denervation contributes to the impairment of response inhibition, via changes in the prefrontal cortex and its subcortical connections. Previous work in Parkinson’s disease found that the selective noradrenaline reuptake inhibitor atomoxetine could improve response inhibition, gambling decisions and reflection impulsivity. Here we tested the hypotheses that atomoxetine can restore functional brain networks for response inhibition in Parkinson’s disease, and that both structural and functional connectivity determine the behavioural effect. In a randomized, double-blind placebo-controlled crossover study, 19 patients with mild-to-moderate idiopathic Parkinson’s disease underwent functional magnetic resonance imaging during a stop-signal task, while on their usual dopaminergic therapy. Patients received 40 mg atomoxetine or placebo, orally. This regimen anticipates that noradrenergic therapies for behavioural symptoms would be adjunctive to, not a replacement for, dopaminergic therapy. Twenty matched control participants provided normative data. Arterial spin labelling identified no significant changes in regional perfusion. We assessed functional interactions between key frontal and subcortical brain areas for response inhibition, by comparing 20 dynamic causal models of the response inhibition network, inverted to the functional magnetic resonance imaging data and compared using random effects model selection. We found that the normal interaction between pre-supplementary motor cortex and the inferior frontal gyrus was absent in Parkinson’s disease patients on placebo (despite dopaminergic therapy), but this connection was restored by atomoxetine. The behavioural change in response inhibition (improvement indicated by reduced
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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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Atomoxetine restores the response inhibition network in Parkinson's disease.
Rae, Charlotte L; Nombela, Cristina; Rodríguez, Patricia Vázquez; Ye, Zheng; Hughes, Laura E; Jones, P Simon; Ham, Timothy; Rittman, Timothy; Coyle-Gilchrist, Ian; Regenthal, Ralf; Sahakian, Barbara J; Barker, Roger A; Robbins, Trevor W; Rowe, James B
2016-08-01
Parkinson's disease impairs the inhibition of responses, and whilst impulsivity is mild for some patients, severe impulse control disorders affect ∼10% of cases. Based on preclinical models we proposed that noradrenergic denervation contributes to the impairment of response inhibition, via changes in the prefrontal cortex and its subcortical connections. Previous work in Parkinson's disease found that the selective noradrenaline reuptake inhibitor atomoxetine could improve response inhibition, gambling decisions and reflection impulsivity. Here we tested the hypotheses that atomoxetine can restore functional brain networks for response inhibition in Parkinson's disease, and that both structural and functional connectivity determine the behavioural effect. In a randomized, double-blind placebo-controlled crossover study, 19 patients with mild-to-moderate idiopathic Parkinson's disease underwent functional magnetic resonance imaging during a stop-signal task, while on their usual dopaminergic therapy. Patients received 40 mg atomoxetine or placebo, orally. This regimen anticipates that noradrenergic therapies for behavioural symptoms would be adjunctive to, not a replacement for, dopaminergic therapy. Twenty matched control participants provided normative data. Arterial spin labelling identified no significant changes in regional perfusion. We assessed functional interactions between key frontal and subcortical brain areas for response inhibition, by comparing 20 dynamic causal models of the response inhibition network, inverted to the functional magnetic resonance imaging data and compared using random effects model selection. We found that the normal interaction between pre-supplementary motor cortex and the inferior frontal gyrus was absent in Parkinson's disease patients on placebo (despite dopaminergic therapy), but this connection was restored by atomoxetine. The behavioural change in response inhibition (improvement indicated by reduced stop-signal reaction
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Park, Sun-Kyeong; Lee, Min-Young; Jang, Eun-Jin; Kim, Hye-Lin; Ha, Dong-Mun; Lee, Eui-Kyung
2017-01-01
The purpose of this study was to compare the discontinuation rates of tofacitinib and biologics (tumour necrosis factor inhibitors (TNFi), abatacept, rituximab, and tocilizumab) in rheumatoid arthritis (RA) patients considering inadequate responses (IRs) to previous treatment(s). Randomised controlled trials of tofacitinib and biologics - reporting at least one total discontinuation, discontinuation due to lack of efficacy (LOE), and discontinuation due to adverse events (AEs) - were identified through systematic review. The analyses were conducted for patients with IRs to conventional synthetic disease-modifying anti-rheumatic drugs (cDMARDs) and for patients with biologics-IR, separately. Bayesian network meta-analysis was used to estimate rate ratio (RR) of a biologic relative to tofacitinib with 95% credible interval (CrI), and probability of RR being tofacitinib and biologics in the cDMARDs-IR group. In the biologics-IR group, however, TNFi (RR 0.17, 95% CrI 0.01-3.61, P[RRtofacitinib did. Despite the difference, discontinuation cases owing to LOE and AEs revealed that tofacitinib was comparable to the biologics. The comparability of discontinuation rate between tofacitinib and biologics was different based on previous treatments and discontinuation reasons: LOE, AEs, and total (due to other reasons). Therefore, those factors need to be considered to decide the optimal treatment strategy.
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Poder, Thomas G; Fisette, Jean-François
2016-07-01
To perform a cost-effectiveness analysis to help hospital decision-makers with regard to the use of drug-coated balloons compared with bare metal stents and uncoated balloons for femoropopliteal occlusive disease. Clinical outcomes were extracted from the results of meta-analyses already published, and cost units are those used in the Quebec healthcare network. The literature review was limited to the last four years to obtain the most recent data. The cost-effectiveness analysis was based on a 2-year perspective, and risk factors of reintervention were considered. The cost-effectiveness analysis indicated that drug-coated balloons were generally more efficient than bare metal stents, particularly for patients with higher risk of reintervention (up to CAD$1686 per patient TASC II C or D). Compared with uncoated balloons, results indicated that drug-coated balloons were more efficient if the reintervention rate associated with uncoated balloons is very high and for patients with higher risk of reintervention (up to CAD$3301 per patient). The higher a patient's risk of reintervention, the higher the savings associated with the use of a drug-coated balloon will be. For patients at lower risk, the uncoated balloon strategy is still recommended as a first choice for endovascular intervention.
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International Nuclear Information System (INIS)
Fukuda, Eriko; Tanimoto, Jun; Akimoto, Mitsuhiro
2015-01-01
Highlights: • We construct a model using epidemiological and vaccination dynamics. • We study effects of information propagation networks on disease propagation. • Information propagation networks affect the spatial structures of vaccinators. • Disease transmission network affects the information propagation network results. • Vaccination cost does not alter the effects of network-topology symmetry breaking. - Abstract: In previous epidemiological studies that address adaptive vaccination decisions, individuals generally act within a single network, which models the population structure. However, in reality, people are typically members of multiplex networks, which have various community structures. For example, a disease transmission network, which directly transmits infectious diseases, does not necessarily correspond with an information propagation network, in which individuals directly or indirectly exchange information concerning health conditions and vaccination strategies. The latter network may also be used for strategic interaction (strategy adaptation) concerning vaccination. Therefore, in order to reflect this feature, we consider the vaccination dynamics of structured populations whose members simultaneously belong to two types of networks: disease transmission and information propagation. Applying intensive numerical calculations, we determine that if the disease transmission network is modeled using a regular graph, such as a lattice population or random regular graph containing individuals of equivalent degrees, individuals should base their vaccination decisions on a different type of network. However, if the disease transmission network is a degree-heterogeneous graph, such as the Barabási–Albert scale-free network, which has a heterogeneous degree according to power low, then using the same network for information propagation more effectively prevents the spread of epidemics. Furthermore, our conclusions are unaffected by the relative
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Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals.
Zeng, Tao; Zhang, Wanwei; Yu, Xiangtian; Liu, Xiaoping; Li, Meiyi; Chen, Luonan
2016-07-01
Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically 'small-sample size in high-dimension space', i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as 'large-sample size in low-dimension space', i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression
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Patient-derived stem cells: pathways to drug discovery for brain diseases
Directory of Open Access Journals (Sweden)
Alan eMackay-Sim
2013-03-01
Full Text Available The concept of drug discovery through stem cell biology is based on technological developments whose genesis is now coincident. The first is automated cell microscopy with concurrent advances in image acquisition and analysis, known as high content screening (HCS. The second is patient-derived stem cells for modelling the cell biology of brain diseases. HCS has developed from the requirements of the pharmaceutical industry for high throughput assays to screen thousands of chemical compounds in the search for new drugs. HCS combines new fluorescent probes with automated microscopy and computational power to quantify the effects of compounds on cell functions. Stem cell biology has advanced greatly since the discovery of genetic reprogramming of somatic cells into induced pluripotent stem cells (iPSCs. There is now a rush of papers describing their generation from patients with various diseases of the nervous system. Although the majority of these have been genetic diseases, iPSCs have been generated from patients with complex diseases (schizophrenia and sporadic Parkinson’s disease. Some genetic diseases are also modelled in embryonic stem cells generated from blastocysts rejected during in vitro fertilisation. Neural stem cells have been isolated from post-mortem brain of Alzheimer’s patients and neural stem cells generated from biopsies of the olfactory organ of patients is another approach. These olfactory neurosphere-derived cells demonstrate robust disease-specific phenotypes in patients with schizophrenia and Parkinson’s disease. High content screening is already in use to find small molecules for the generation and differentiation of embryonic stem cells and induced pluripotent stem cells. The challenges for using stem cells for drug discovery are to develop robust stem cell culture methods that meet the rigorous requirements for repeatable, consistent quantities of defined cell types at the industrial scale necessary for high
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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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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
Inhalation of nanoparticle-based drug for lung cancer treatment: Advantages and challenges
Directory of Open Access Journals (Sweden)
Wing-Hin Lee
2015-12-01
Full Text Available Ever since the success of developing inhalable insulin, drug delivery via pulmonary administration has become an attractive route to treat chronic diseases. Pulmonary delivery system for nanotechnology is a relatively new concept especially when applicable to lung cancer therapy. Nano-based systems such as liposome, polymeric nanoparticles or micelles are strategically designed to enhance the therapeutic index of anti-cancer drugs through improvement of their bioavailability, stability and residency at targeted lung regions. Along with these benefits, nano-based systems also provide additional diagnostic advantages during lung cancer treatment, including imaging, screening and drug tracking. Nevertheless, delivery of nano-based drugs via pulmonary administration for lung cancer therapy is still in its infancy and numerous challenges are expected. Pharmacology, immunology, toxicology and large-scale manufacturing (stability and activity of drugs are some aspects in nanotechnology that should be taken into consideration for the development of inhalable nano-based chemotherapeutic drugs. This review will focus on the current inhalable nano-based drugs for lung cancer treatment.
Structure-based drug design approach to target toll-like receptor ...
African Journals Online (AJOL)
TLRs are now viewed as potential therapeutic targets in the treatment of autoimmune diseases. This ... Vascular endothelial growth factor. NMR .... induces the release of tumor necrosis factor ... Alternative anticancer drugs called CpG-based.
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.
Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali
2018-06-01
Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.
Orphan drugs: trends and issues in drug development.
Rana, Proteesh; Chawla, Shalini
2018-04-12
Research in rare diseases has contributed substantially toward the current understanding in the pathophysiology of the common diseases. However, medical needs of patients with rare diseases have always been neglected by the society and pharmaceutical industries based on their small numbers and unprofitability. The Orphan Drug Act (1983) was the first serious attempt to address the unmet medical needs for patients with rare diseases and to provide impetus for the pharmaceutical industry to promote orphan drug development. The process of drug development for rare diseases is no different from common diseases but involves significant cost and infrastructure. Further, certain aspect of drug research may not be feasible for the rare diseases. The drug-approving authority must exercise their scientific judgment and ensure due flexibility while evaluating data at various stages of orphan drug development. The emergence of patent cliff combined with the government incentives led the pharmaceutical industry to realize the good commercial prospects in developing an orphan drug despite the small market size. Indeed, many drugs that were given orphan designation ended up being blockbusters. The orphan drug market is projected to reach $178 billion by 2020, and the prospects of research and development in rare diseases appears to be quite promising and rewarding.
Annang, F; Pérez-Moreno, G; García-Hernández, R; Cordon-Obras, C; Martín, J; Tormo, J R; Rodríguez, L; de Pedro, N; Gómez-Pérez, V; Valente, M; Reyes, F; Genilloud, O; Vicente, F; Castanys, S; Ruiz-Pérez, L M; Navarro, M; Gamarro, F; González-Pacanowska, D
2015-01-01
African trypanosomiasis, leishmaniasis, and Chagas disease are 3 neglected tropical diseases for which current therapeutic interventions are inadequate or toxic. There is an urgent need to find new lead compounds against these diseases. Most drug discovery strategies rely on high-throughput screening (HTS) of synthetic chemical libraries using phenotypic and target-based approaches. Combinatorial chemistry libraries contain hundreds of thousands of compounds; however, they lack the structural diversity required to find entirely novel chemotypes. Natural products, in contrast, are a highly underexplored pool of unique chemical diversity that can serve as excellent templates for the synthesis of novel, biologically active molecules. We report here a validated HTS platform for the screening of microbial extracts against the 3 diseases. We have used this platform in a pilot project to screen a subset (5976) of microbial extracts from the MEDINA Natural Products library. Tandem liquid chromatography-mass spectrometry showed that 48 extracts contain potentially new compounds that are currently undergoing de-replication for future isolation and characterization. Known active components included actinomycin D, bafilomycin B1, chromomycin A3, echinomycin, hygrolidin, and nonactins, among others. The report here is, to our knowledge, the first HTS of microbial natural product extracts against the above-mentioned kinetoplastid parasites. © 2014 Society for Laboratory Automation and Screening.
Early grey matter changes in structural covariance networks in Huntington's disease.
Coppen, Emma M; van der Grond, Jeroen; Hafkemeijer, Anne; Rombouts, Serge A R B; Roos, Raymund A C
2016-01-01
Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. Structural magnetic resonance imaging data of premanifest HD ( n = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD.
Modules, networks and systems medicine for understanding disease and aiding diagnosis
DEFF Research Database (Denmark)
Gustafsson, Mika; Nestor, Colm E.; Zhang, Huan
2014-01-01
Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics dat...
Modules, networks and systems medicine for understanding disease and aiding diagnosis
Gustafsson, Mika; Nestor, Colm E.; Zhang, Huan; Barabási, Albert-László; Baranzini, Sergio; Brunak, Sören; Chung, Kian Fan; Federoff, Howard J.; Gavin, Anne-Claude; Meehan, Richard R.; Picotti, Paola; Pujana, Miguel Àngel; Rajewsky, Nikolaus; Smith, Kenneth Gc; Sterk, Peter J.; Villoslada, Pablo; Benson, Mikael
2014-01-01
Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data
From crystal to compound: structure-based antimalarial drug discovery.
Drinkwater, Nyssa; McGowan, Sheena
2014-08-01
Despite a century of control and eradication campaigns, malaria remains one of the world's most devastating diseases. Our once-powerful therapeutic weapons are losing the war against the Plasmodium parasite, whose ability to rapidly develop and spread drug resistance hamper past and present malaria-control efforts. Finding new and effective treatments for malaria is now a top global health priority, fuelling an increase in funding and promoting open-source collaborations between researchers and pharmaceutical consortia around the world. The result of this is rapid advances in drug discovery approaches and technologies, with three major methods for antimalarial drug development emerging: (i) chemistry-based, (ii) target-based, and (iii) cell-based. Common to all three of these approaches is the unique ability of structural biology to inform and accelerate drug development. Where possible, SBDD (structure-based drug discovery) is a foundation for antimalarial drug development programmes, and has been invaluable to the development of a number of current pre-clinical and clinical candidates. However, as we expand our understanding of the malarial life cycle and mechanisms of resistance development, SBDD as a field must continue to evolve in order to develop compounds that adhere to the ideal characteristics for novel antimalarial therapeutics and to avoid high attrition rates pre- and post-clinic. In the present review, we aim to examine the contribution that SBDD has made to current antimalarial drug development efforts, covering hit discovery to lead optimization and prevention of parasite resistance. Finally, the potential for structural biology, particularly high-throughput structural genomics programmes, to identify future targets for drug discovery are discussed.
Polymer based drug delivery systems for mycobacterial infections.
Pandey, Rajesh; Khuller, G K
2004-07-01
In the last decade, polymer based technologies have found wide biomedical applications. Polymers, whether synthetic (e.g. polylactide-co-glycolide or PLG) or natural (e.g. alginate, chitosan etc.), have the property of encapsulating a diverse range of molecules of biological interest and bear distinct therapeutic advantages such as controlled release of drugs, protection against the premature degradation of drugs and reduction in drug toxicity. These are important considerations in the long-duration treatment of chronic infectious diseases such as tuberculosis in which patient non-compliance is the major obstacle to successful chemotherapy. Antitubercular drugs, singly or in combination, have been encapsulated in polymers to provide controlled drug release and the system also offers the flexibility of selecting various routes of administration such as oral, subcutaneous and aerosol. The present review highlights the approaches towards the preparation of polymeric antitubercular drug delivery systems, emphasizing how the route of administration may influence drug bioavailability as well as the chemotherapeutic efficacy. In addition, the pros and cons of the various delivery systems are also discussed.
Fragment-based approaches to TB drugs.
Marchetti, Chiara; Chan, Daniel S H; Coyne, Anthony G; Abell, Chris
2018-02-01
Tuberculosis is an infectious disease associated with significant mortality and morbidity worldwide, particularly in developing countries. The rise of antibiotic resistance in Mycobacterium tuberculosis (Mtb) urgently demands the development of new drug leads to tackle resistant strains. Fragment-based methods have recently emerged at the forefront of pharmaceutical development as a means to generate more effective lead structures, via the identification of fragment molecules that form weak but high quality interactions with the target biomolecule and subsequent fragment optimization. This review highlights a number of novel inhibitors of Mtb targets that have been developed through fragment-based approaches in recent years.
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Back propagation artificial neural network for community Alzheimer's disease screening in China.
Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao
2013-01-25
Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.
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Back propagation artificial neural network for community Alzheimer's disease screening in China★
Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao
2013-01-01
Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868–0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community. PMID:25206598
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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)
Enserink Remko
2012-10-01
Full Text Available Abstract Background Day care-associated infectious diseases are widely recognized as a public health problem but rarely studied. Insights into their dynamics and their association with the day care setting are important for effective decision making in management of infectious disease control. This paper describes the purpose, design and potential of our national multi-center, day care-based sentinel surveillance network for infectious diseases (the KIzSS network. The aim of the KIzSS network is to acquire a long-term insight into the syndromic and microbiological aspects of day care-related infectious diseases and associated disease burden and to model these aspects with day care setting characteristics. Methods/design The KIzSS network applies a prospective cohort design, following day care centers rather than individual children or staff members over time. Data on infectious disease symptoms and related morbidity (children and staff, medical consumption, absenteeism and circulating enteric pathogens (children are collected on a daily, weekly or monthly basis. Every two years, a survey is performed to assess the characteristics of participating day care centers. Discussion The KIzSS network offers a unique potential to study infectious disease dynamics in the day care setting over a sustained period of time. The created (biodatabases will help us to assess day care-related disease burden of infectious diseases among attending children and staff and their relation with the day care setting. This will support the much needed development of evidence-based and pragmatic guidelines for infectious disease control in day care centers.
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Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis
Directory of Open Access Journals (Sweden)
Jae Kwon Kim
2017-01-01
Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.
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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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Wang, Yin-Ying; Bai, Hong; Zhang, Run-Zhi; Yan, Hong; Ning, Kang; Zhao, Xing-Ming
2017-11-07
With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.
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Directory of Open Access Journals (Sweden)
Michael I Miller
2015-05-01
Full Text Available This paper examines MRI analysis of neurodegeneration in Alzheimer’s Disease (AD in a network of structures within the medial temporal lobe using diffeomorphometry methods coupled with high-field atlasing in which the entorhinal cortex is partitioned into nine subareas. The morphometry markers for three groups of subjects (controls, preclinical AD and symptomatic AD are indexed to template coordinates measured with respect to these nine subareas. The location and timing of changes are examined within the subareas as it pertains to the classic Braak and Braak staging by comparing the three groups. We demonstrate that the earliest preclinical changes in the population occur in the lateral most sulcal extent in the entorhinal cortex (alluded to as trans entorhinal cortex by Braak and Braak, and then proceeds medially which is consistent with the Braak and Braak staging. We use high field 11T atlasing to demonstrate that the network changes are occurring at the junctures of the substructures in this medial temporal lobe network. Temporal progression of the disease through the network is also examined via changepoint analysis demonstrating earliest changes in entorhinal cortex. The differential expression of rate of atrophy with progression signaling the changepoint time across the network is demonstrated to be signaling in the intermediate caudal subarea of the entorhinal cortex, which has been noted to be proximal to the hippocampus. This coupled to the findings of the nearby basolateral involvement in amygdala demonstrates the selectivity of neurodegeneration in early AD.
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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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Isgut, Monica; Rao, Mukkavilli; Yang, Chunhua; Subrahmanyam, Vangala; Rida, Padmashree C G; Aneja, Ritu
2018-03-01
Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product-based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs. © 2017 Wiley Periodicals, Inc.
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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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Cloud networking understanding cloud-based data center networks
Lee, Gary
2014-01-01
Cloud Networking: Understanding Cloud-Based Data Center Networks explains the evolution of established networking technologies into distributed, cloud-based networks. Starting with an overview of cloud technologies, the book explains how cloud data center networks leverage distributed systems for network virtualization, storage networking, and software-defined networking. The author offers insider perspective to key components that make a cloud network possible such as switch fabric technology and data center networking standards. The final chapters look ahead to developments in architectures
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Predicting Drug-Target Interactions Based on Small Positive Samples.
Hu, Pengwei; Chan, Keith C C; Hu, Yanxing
2018-01-01
A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance
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Sustainable rare diseases business and drug access: no time for misconceptions.
Rollet, Pierrick; Lemoine, Adrien; Dunoyer, Marc
2013-07-23
Legislative incentives enacted in Europe through the Regulation (EC) No. 141/2000 to incentivize orphan drug development have over the last 12 years constituted a powerful impetus toward R&D directed at the rare diseases population. However, despite therapeutic promises contained in these projects and significant economic impact linked to burgeoning R&D expenditures, the affordability and value of OMPs has become a topic of health policy debate in Europe fueled by the perception that OMPs have high acquisition costs, and by misconceptions around pricing dynamics and rare-diseases business models. In order to maintain sustainable patient access to new and innovative therapies, it is essential to address these misconceptions, and to ensure the successful continuation of a dynamic OMPs R&D within rare-diseases public health policy. Misconceptions abound regarding the pricing of rare diseases drugs and reflect a poor appreciation of the R&D model and the affordability and value of OMPs. Simulation of potential financial returns of small medium sized rare diseases companies focusing on high priced drugs show that their economic returns are likely to be close to their cost of capital. Research in rare diseases is a challenging endeavour characterised by high fixed costs in which companies accrue substantial costs for several years before potentially generating returns from the fruits of their investments. Although heavily dependent upon R&D capabilities of each individual company or R&D organization, continuous flow of R&D financial investment should allow industry to increasingly include efficiencies in research and development in cost considerations to its customers. Industry should also pro-actively work on facilitating development of a specific value based pricing approach to help understanding what constitute value in rare diseases. Policy makers must reward innovation based upon unmet need and patient outcome. Broader understanding by clinicians, the public, and
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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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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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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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Penrod, Nadia M; Greene, Casey S; Moore, Jason H
2014-01-01
Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination
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Meijer, Willemijn M; Cornel, Martina C; Dolk, Helen; de Walle, Hermien E K; Armstrong, Nicola C; de Jong-van den Berg, Lolkje T W
2006-09-01
European Surveillance of Congenital Anomalies (EUROCAT) is a network of population-based congenital anomaly registries in Europe surveying more than 1 million births per year, or 25% of the births in the European Union. This paper describes the potential of the EUROCAT collaboration for pharmacoepidemiology and drug safety surveillance. The 34 full members and 6 associate members of the EUROCAT network were sent a questionnaire about their data sources on drug exposure and on drug coding. Available data on drug exposure during the first trimester available in the central EUROCAT database for the years 1996-2000 was summarised for 15 out of 25 responding full members. Of the 40 registries, 29 returned questionnaires (25 full and 4 associate members). Four of these registries do not collect data on maternal drug use. Of the full members, 15 registries use the EUROCAT drug code, 4 use the international ATC drug code, 3 registries use another coding system and 7 use a combination of these coding systems. Obstetric records are the most frequently used sources of drug information for the registries, followed by interviews with the mother. Only one registry uses pharmacy data. Percentages of cases with drug exposure (excluding vitamins/minerals) varied from 4.4% to 26.0% among different registries. The categories of drugs recorded varied widely between registries. Practices vary widely between registries regarding recording drug exposure information. EUROCAT has the potential to be an effective collaborative framework to contribute to post-marketing drug surveillance in relation to teratogenic effects, but work is needed to implement ATC drug coding more widely, and to diversify the sources of information used to determine drug exposure in each registry.
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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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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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Decoding signalling networks by mass spectrometry-based proteomics
DEFF Research Database (Denmark)
Choudhary, Chuna Ram; Mann, Matthias
2010-01-01
Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system......-wide characterization of signalling events at the levels of post-translational modifications, protein-protein interactions and changes in protein expression. This technology delivers accurate and unbiased information about the quantitative changes of thousands of proteins and their modifications in response to any...... perturbation. Current studies focus on phosphorylation, but acetylation, methylation, glycosylation and ubiquitylation are also becoming amenable to investigation. Large-scale proteomics-based signalling research will fundamentally change our understanding of signalling networks....
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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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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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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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Developing novel blood-based biomarkers for Alzheimer's disease
DEFF Research Database (Denmark)
Snyder, Heather M; Carrillo, Maria C; Grodstein, Francine
2014-01-01
Alzheimer's disease is the public health crisis of the 21st century. There is a clear need for a widely available, inexpensive and reliable method to diagnosis Alzheimer's disease in the earliest stages, track disease progression, and accelerate clinical development of new therapeutics. One avenue...... of research being explored is blood based biomarkers. In April 2012, the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation convened top scientists from around the world to discuss the state of blood based biomarker development. This manuscript summarizes the meeting and the resultant...
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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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Drug and bioactive molecule screening based on a bioelectrical impedance cell culture platform
Directory of Open Access Journals (Sweden)
Ramasamy S
2014-12-01
Full Text Available Sakthivel Ramasamy,1 Devasier Bennet,1 Sanghyo Kim1,2 1Department of Bionanotechnology, Gachon University, Gyeonggi-Do, Republic of Korea; 2Graduate Gachon Medical Research Institute, Gil Medical Center, Incheon, Republic of Korea Abstract: This review will present a brief discussion on the recent advancements of bioelectrical impedance cell-based biosensors, especially the electric cell-substrate impedance sensing (ECIS system for screening of various bioactive molecules. The different technical integrations of various chip types, working principles, measurement systems, and applications for drug targeting of molecules in cells are highlighted in this paper. Screening of bioactive molecules based on electric cell-substrate impedance sensing is a trial-and-error process toward the development of therapeutically active agents for drug discovery and therapeutics. In general, bioactive molecule screening can be used to identify active molecular targets for various diseases and toxicity at the cellular level with nanoscale resolution. In the innovation and screening of new drugs or bioactive molecules, the activeness, the efficacy of the compound, and safety in biological systems are the main concerns on which determination of drug candidates is based. Further, drug discovery and screening of compounds are often performed in cell-based test systems in order to reduce costs and save time. Moreover, this system can provide more relevant results in in vivo studies, as well as high-throughput drug screening for various diseases during the early stages of drug discovery. Recently, MEMS technologies and integration with image detection techniques have been employed successfully. These new technologies and their possible ongoing transformations are addressed. Select reports are outlined, and not all the work that has been performed in the field of drug screening and development is covered. Keywords: screening of bioactive agents, impedance-based cell
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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.
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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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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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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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Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming
2018-04-11
Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree
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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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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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An attention-based effective neural model for drug-drug interactions extraction.
Zheng, Wei; Lin, Hongfei; Luo, Ling; Zhao, Zhehuan; Li, Zhengguang; Zhang, Yijia; Yang, Zhihao; Wang, Jian
2017-10-10
Drug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various methods to classify DDIs, the classification performance with regard to DDIs in long and complex sentences is still unsatisfactory. In this study, we propose an effective model that classifies DDIs from the literature by combining an attention mechanism and a recurrent neural network with long short-term memory (LSTM) units. In our approach, first, a candidate-drug-oriented input attention acting on word-embedding vectors automatically learns which words are more influential for a given drug pair. Next, the inputs merging the position- and POS-embedding vectors are passed to a bidirectional LSTM layer whose outputs at the last time step represent the high-level semantic information of the whole sentence. Finally, a softmax layer performs DDI classification. Experimental results from the DDIExtraction 2013 corpus show that our system performs the best with respect to detection and classification (84.0% and 77.3%, respectively) compared with other state-of-the-art methods. In particular, for the Medline-2013 dataset with long and complex sentences, our F-score far exceeds those of top-ranking systems by 12.6%. Our approach effectively improves the performance of DDI classification tasks. Experimental analysis demonstrates that our model performs better with respect to recognizing not only close-range but also long-range patterns among words, especially for long, complex and compound sentences.
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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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Towards a Hybrid Agent-based Model for Mosquito Borne Disease.
Mniszewski, S M; Manore, C A; Bryan, C; Del Valle, S Y; Roberts, D
2014-07-01
Agent-based models (ABM) are used to simulate the spread of infectious disease through a population. Detailed human movement, demography, realistic business location networks, and in-host disease progression are available in existing ABMs, such as the Epidemic Simulation System (EpiSimS). These capabilities make possible the exploration of pharmaceutical and non-pharmaceutical mitigation strategies used to inform the public health community. There is a similar need for the spread of mosquito borne pathogens due to the re-emergence of diseases such as chikungunya and dengue fever. A network-patch model for mosquito dynamics has been coupled with EpiSimS. Mosquitoes are represented as a "patch" or "cloud" associated with a location. Each patch has an ordinary differential equation (ODE) mosquito dynamics model and mosquito related parameters relevant to the location characteristics. Activities at each location can have different levels of potential exposure to mosquitoes based on whether they are inside, outside, or somewhere in-between. As a proof of concept, the hybrid network-patch model is used to simulate the spread of chikungunya through Washington, DC. Results are shown for a base case, followed by varying the probability of transmission, mosquito count, and activity exposure. We use visualization to understand the pattern of disease spread.
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Voshaar, Marieke; Vriezekolk, Johanna; van Dulmen, Sandra; van den Bemt, Bart; van de Laar, Mart
2016-10-21
Although disease-modifying anti-rheumatic drugs (DMARDs) are the cornerstone of treatment for inflammatory rheumatic diseases, medication adherence to DMARDs is often suboptimal. Effective interventions to improve adherence to DMARDs are lacking, and new targets are needed to improve adherence. The aim of the present study was to explore patients' barriers and facilitators of optimal DMARD use. These factors might be used as targets for adherence interventions. In a mixed method study design, patients (n = 120) with inflammatory arthritis (IA) completed a questionnaire based on an existing adapted Theoretical Domains Framework (TDF) to identify facilitators and barriers of DMARD use. A subgroup of these patients (n = 21) participated in focus groups to provide insights into their facilitators and barriers. The answers to the questionnaires and responses of the focus groups were thematically coded by three researchers independently and subsequently categorized. The barriers and facilitators that were reported by IA patients presented large inter-individual variations. The identified barriers and facilitators could be captured in the following domains based on an adapted TDF: (i) knowledge, (ii) emotions, (iii) attention, memory, and decision processes, (iv) social influences, (v) beliefs about capability, (vi) beliefs about consequences, (vii) motivation and goals, (viii) goal conflict, (ix) environmental context and resources, and (x) skills. Patients with IA have a variety of barriers and facilitators with regard to their DMARD use. All of these barriers and facilitators could be categorized into adapted domains of the TDF. Interventions that address individual facilitators and barriers, based on capability, opportunity, and motivation, are needed to develop strategies for medication adherence that are tailored to individual patient needs.
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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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Mina, George S; Watti, Hussam; Soliman, Demiana; Shewale, Anand; Atkins, Jessica; Reddy, Pratap; Dominic, Paari
2018-01-05
Most data guiding revascularization of multivessel disease (MVD) and/or left main disease (LMD) favor coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI). However, those data are based on trials comparing CABG to bare metal stents (BMS) or old generation drug eluting stents (OG-DES). Hence, it is essential to outcomes of CABG to those of new generation drug eluting stents (NG-DES). We searched PUBMED and Cochrane database for trials evaluating revascularization of MVD and/or LMD with CABG and/or PCI. A Bayesian network meta-analysis was performed to calculate odds ratios (OR) and 95% credible intervals (CrI). Primary outcome was major adverse cardiovascular events (MACE) at 3-5 years. Secondary outcomes were mortality, cerebrovascular accidents (CVA), myocardial infarction (MI) and repeat revascularization. We included 10 trials with a total of 9287 patients. CABG was associated with lower MACE when compared to BMS or OG-DES. However, MACE was not significantly different between CABG and NG-DES (OR 0.79, CrI 0.45-1.40). Moreover, there were no significant differences between CABG and NG-DES in mortality (OR 0.78, CrI 0.45-1.37), CVA (OR 0.93 CrI 0.35-2.2) or MI (OR 0.6, CrI 0.17-2.0). On the other hand, CABG was associated with lower repeat revascularization (OR 0.55, CrI 0.36-0.84). Our study suggests that NG-DES is an acceptable alternative to CABG in patients with MVD and/or LMD. However, repeat revascularization remains to be lower with CABG than with PCI. Copyright © 2018. Published by Elsevier Inc.
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Drug target ontology to classify and integrate drug discovery data
DEFF Research Database (Denmark)
Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande
2017-01-01
using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem...... of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target...... characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein...
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Schneider, Lon S
2014-03-01
The February 2013 Food and Drug Administration (FDA) draft guidance for developing drugs for early-stage Alzheimer's disease (AD) creates certain challenges as they guide toward the use of one cognitive outcome to gain accelerated marketing approval for preclinical AD drugs, and a composite clinical scale - the Clinical Dementia Rating Scale in particular - for the primary outcome for prodromal AD clinical trials. In light of the developing knowledge regarding early stage diagnoses and clinical trials outcomes, we recommend that FDA describe its requirements for validating preclinical AD diagnoses for drug development purposes, maintain the principle for requiring coprimary outcomes, and encourage the advancement of outcomes for early stage AD trials. The principles for drug development for early stage AD should not differ from those for clinical AD, especially as the diagnoses of prodromal and early AD impinge on each other. The FDA should not recommend that a composite scale be used as a sole primary efficacy outcome to support a marketing claim unless it requires that the cognitive and functional components of such a scale are demonstrated to be individually meaningful. The current draft guidelines may inadvertently constrain efforts to better assess the clinical effects of new drugs and inhibit innovation in an area where evidence-based clinical research practices are still evolving. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
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Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo
2016-01-01
The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.
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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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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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Simulating individual-based models of epidemics in hierarchical networks
Quax, R.; Bader, D.A.; Sloot, P.M.A.
2009-01-01
Current mathematical modeling methods for the spreading of infectious diseases are too simplified and do not scale well. We present the Simulator of Epidemic Evolution in Complex Networks (SEECN), an efficient simulator of detailed individual-based models by parameterizing separate dynamics
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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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Assessment of Web-Based Consumer Reviews as a Resource for Drug Performance
Adusumalli, Swarnaseetha; Lee, HueyTyng; Hoi, Qiangze; Koo, Si-Lin; Tan, Iain Beehuat
2015-01-01
Background Some health websites provide a public forum for consumers to post ratings and reviews on drugs. Drug reviews are easily accessible and comprehensible, unlike clinical trials and published literature. Because the public increasingly uses the Internet as a source of medical information, it is important to know whether such information is reliable. Objective We aim to examine whether Web-based consumer drug ratings and reviews can be used as a resource to compare drug performance. Methods We analyzed 103,411 consumer-generated reviews on 615 drugs used to treat 249 disease conditions from the health website WebMD. Statistical analysis identified 427 drug pairs from 24 conditions for which two drugs treating the same condition had significantly and substantially different satisfaction ratings (with at least a half-point difference between Web-based ratings and Paddictive properties were rated higher than their counterparts in Web-based reviews, and (3) second-line or alternative drugs were rated higher. In addition, Web-based ratings indicated drug delivery problems. If FDA black box warning labels are used to resolve disagreements between publications and online trends, the concordance rate increases to 71% (55/77) (Pmanufacturers to assess the performance of a drug. However, one should be cautious to rely solely on consumer reviews as ratings can be strongly influenced by the consumer experience. PMID:26319108
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Personalized Network-Based Treatments in Oncology
DEFF Research Database (Denmark)
Robin, Xavier; Creixell, Pau; Radetskaya, Oxana
2013-01-01
Network medicine aims at unraveling cell signaling networks to propose personalized treatments for patients suffering from complex diseases. In this short review, we show the relevance of network medicine to cancer treatment by outlining the potential convergence points of the most recent technol...
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[Non-Helicobacter pylori, Non-nonsteroidal Anti-inflammatory Drug Peptic Ulcer Disease].
Chang, Young Woon
2016-06-25
Non-Helicobacter pylori, non-NSAID peptic ulcer disease (PUD), termed idiopathic PUD, is increasing in Korea. Diagnosis is based on exclusion of common causes such as H. pylori infection, infection with other pathogens, surreptitious ulcerogenic drugs, malignancy, and uncommon systemic diseases with upper gastrointestinal manifestations. The clinical course of idiopathic PUD is delayed ulcer healing, higher recurrence, higher re-bleeding after initial ulcer healing, and higher mortality than the other types of PUD. Genetic predisposition, older age, chronic mesenteric ischemia, cigarette smoking, concomitant systemic diseases, and psychological stress are considered risk factors for idiopathic PUD. Diagnosis of idiopathic PUD should systematically explore all possible causes. Management of this disease is to treat underlying disease followed by regular endoscopic surveillance to confirm ulcer healing. Continuous proton pump inhibitor therapy is an option for patients who respond poorly to the standard ulcer regimen.
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A hybrid model based on neural networks for biomedical relation extraction.
Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang
2018-05-01
Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.
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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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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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Munneke, Marten; Nijkrake, Maarten J; Keus, Samyra Hj; Kwakkel, Gert; Berendse, Henk W; Roos, Raymund Ac; Borm, George F; Adang, Eddy M; Overeem, Sebastiaan; Bloem, Bastiaan R
2010-01-01
Many patients with Parkinson's disease are treated with physiotherapy. We have developed a community-based professional network (ParkinsonNet) that involves training of a selected number of expert physiotherapists to work according to evidence-based recommendations, and structured referrals to these trained physiotherapists to increase the numbers of patients they treat. We aimed to assess the efficacy of this approach for improving health-care outcomes. Between February, 2005, and August, 2007, we did a cluster-randomised trial with 16 clusters (defined as community hospitals and their catchment area). Clusters were randomly allocated by use of a variance minimisation algorithm to ParkinsonNet care (n=8) or usual care (n=8). Patients were assessed at baseline and at 8, 16, and 24 weeks of follow-up. The primary outcome was a patient preference disability score, the patient-specific index score, at 16 weeks. Health secondary outcomes were functional mobility, mobility-related quality of life, and total societal costs over 24 weeks. Analysis was by intention to treat. This trial is registered, number NCT00330694. We included 699 patients. Baseline characteristics of the patients were comparable between the ParkinsonNet clusters (n=358) and usual-care clusters (n=341). The primary endpoint was similar for patients within the ParkinsonNet clusters (mean 47.7, SD 21.9) and control clusters (48.3, 22.4). Health secondary endpoints were also similar for patients in both study groups. Total costs over 24 weeks were lower in ParkinsonNet clusters compared with usual-care clusters (difference euro727; 95% CI 56-1399). Implementation of ParkinsonNet networks did not change health outcomes for patients living in ParkinsonNet clusters. However, health-care costs were reduced in ParkinsonNet clusters compared with usual-care clusters. ZonMw; Netherlands Organisation for Scientific Research; Dutch Parkinson's Disease Society; National Parkinson Foundation; Stichting Robuust
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Location based Network Optimizations for Mobile Wireless Networks
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen
selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...
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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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Leveraging social networks for toxicovigilance.
Chary, Michael; Genes, Nicholas; McKenzie, Andrew; Manini, Alex F
2013-06-01
The landscape of drug abuse is shifting. Traditional means of characterizing these changes, such as national surveys or voluntary reporting by frontline clinicians, can miss changes in usage the emergence of novel drugs. Delays in detecting novel drug usage patterns make it difficult to evaluate public policy aimed at altering drug abuse. Increasingly, newer methods to inform frontline providers to recognize symptoms associated with novel drugs or methods of administration are needed. The growth of social networks may address this need. The objective of this manuscript is to introduce tools for using data from social networks to characterize drug abuse. We outline a structured approach to analyze social media in order to capture emerging trends in drug abuse by applying powerful methods from artificial intelligence, computational linguistics, graph theory, and agent-based modeling. First, we describe how to obtain data from social networks such as Twitter using publicly available automated programmatic interfaces. Then, we discuss how to use artificial intelligence techniques to extract content useful for purposes of toxicovigilance. This filtered content can be employed to generate real-time maps of drug usage across geographical regions. Beyond describing the real-time epidemiology of drug abuse, techniques from computational linguistics can uncover ways that drug discussions differ from other online conversations. Next, graph theory can elucidate the structure of networks discussing drug abuse, helping us learn what online interactions promote drug abuse and whether these interactions differ among drugs. Finally, agent-based modeling relates online interactions to psychological archetypes, providing a link between epidemiology and behavior. An analysis of social media discussions about drug abuse patterns with computational linguistics, graph theory, and agent-based modeling permits the real-time monitoring and characterization of trends of drugs of abuse. These
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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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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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Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease.
de Schipper, Laura J; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J
2017-01-01
In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data. 3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD. The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, η p 2 = 0.070) and p = 0.001 (β = - 0.264, η p 2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks. We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.
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Directory of Open Access Journals (Sweden)
Carlos Polanco
2013-01-01
Full Text Available A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008–2010 taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.
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Castañón-González, Jorge Alberto; Macías, Alejandro E.; Samaniego, José Lino; Buhse, Thomas; Villanueva-Martínez, Sebastián
2013-01-01
A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008–2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts. PMID:24069063
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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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Nonbinary Tree-Based Phylogenetic Networks.
Jetten, Laura; van Iersel, Leo
2018-01-01
Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can, for example, represent gene transfer events. Such phylogenetic networks are called tree-based. Here, we consider two possible generalizations of this concept to nonbinary networks, which we call tree-based and strictly-tree-based nonbinary phylogenetic networks. We give simple graph-theoretic characterizations of tree-based and strictly-tree-based nonbinary phylogenetic networks. Moreover, we show for each of these two classes that it can be decided in polynomial time whether a given network is contained in the class. Our approach also provides a new view on tree-based binary phylogenetic networks. Finally, we discuss two examples of nonbinary phylogenetic networks in biology and show how our results can be applied to them.
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Stem cells as a novel tool for drug screening and treatment of degenerative diseases.
Zuba-Surma, Ewa K; Wojakowski, Wojciech; Madeja, Zbigniew; Ratajczak, Mariusz Z
2012-01-01
Degenerative diseases similarly as acute tissue injuries lead to massive cell loss and may cause organ failure of vital organs (e.g., heart, central nervous system). Therefore, they belong to a group of disorders that may significantly benefit from stem cells (SCs)-based therapies. Several stem and progenitor cell populations have already been described as valuable tools for developing therapeutic strategies in regenerative medicine. In particular, pluripotent stem cells (PSCs), including adult-tissue-derived PSCs, neonatal-tissue-derived SCs, embryonic stem cells (ESCs), and recently described induced pluripotent stem cells (iPSCs), are the focus of particular attention because of their capacity to differentiate into all the cell lineages. Although PSCs are predominantly envisioned to be applied for organ regeneration, they may be also successfully employed in drug screening and disease modeling. In particular, adult PSCs and iPSCs derived from patient tissues may not only be a source of cells for autologous therapies but also for individual customized in vitro drug testing and studies on the molecular mechanisms of disease. In this review, we will focus on the potential applications of SCs, especially PSCs i) in regenerative medicine therapies, ii) in studying mechanisms of disease, as well as iii) in drug screening and toxicology tests that are crucial in new drug development. In particular, we will discuss the application of SCs in developing new therapeutic approaches to treat degenerative diseases of the neural system and heart. The advantage of adult PSCs in all the above-mentioned settings is that they can be directly harvested from patient tissues and used not only as a safe non-immunogenic source of cells for therapy but also as tools for personalized drug screening and pharmacological therapies.
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Text mining and network analysis to find functional associations of genes in high altitude diseases.
Bhasuran, Balu; Subramanian, Devika; Natarajan, Jeyakumar
2018-05-02
Travel to elevations above 2500 m is associated with the risk of developing one or more forms of acute altitude illness such as acute mountain sickness (AMS), high altitude cerebral edema (HACE) or high altitude pulmonary edema (HAPE). Our work aims to identify the functional association of genes involved in high altitude diseases. In this work we identified the gene networks responsible for high altitude diseases by using the principle of gene co-occurrence statistics from literature and network analysis. First, we mined the literature data from PubMed on high-altitude diseases, and extracted the co-occurring gene pairs. Next, based on their co-occurrence frequency, gene pairs were ranked. Finally, a gene association network was created using statistical measures to explore potential relationships. Network analysis results revealed that EPO, ACE, IL6 and TNF are the top five genes that were found to co-occur with 20 or more genes, while the association between EPAS1 and EGLN1 genes is strongly substantiated. The network constructed from this study proposes a large number of genes that work in-toto in high altitude conditions. Overall, the result provides a good reference for further study of the genetic relationships in high altitude diseases. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Nano-based theranostics for chronic obstructive lung diseases: challenges and therapeutic potential.
Vij, Neeraj
2011-09-01
The major challenges in the delivery and therapeutic efficacy of nano-delivery systems in chronic obstructive airway conditions are airway defense, severe inflammation and mucous hypersecretion. Chronic airway inflammation and mucous hypersecretion are hallmarks of chronic obstructive airway diseases, including asthma, COPD (chronic obstructive pulmonary disease) and CF (cystic fibrosis). Distinct etiologies drive inflammation and mucous hypersecretion in these diseases, which are further induced by infection or components of cigarette smoke. Controlling chronic inflammation is at the root of treatments such as corticosteroids, antibiotics or other available drugs, which pose the challenge of sustained delivery of drugs to target cells or tissues. In spite of the wide application of nano-based drug delivery systems, very few are tested to date. Targeted nanoparticle-mediated sustained drug delivery is required to control inflammatory cell chemotaxis, fibrosis, protease-mediated chronic emphysema and/or chronic lung obstruction in COPD. Moreover, targeted epithelial delivery is indispensable for correcting the underlying defects in CF and targeted inflammatory cell delivery for controlling other chronic inflammatory lung diseases. We propose that the design and development of nano-based targeted theranostic vehicles with therapeutic, imaging and airway-defense penetrating capability, will be invaluable for treating chronic obstructive lung diseases. This paper discusses a novel nano-theranostic strategy that we are currently evaluating to treat the underlying cause of CF and COPD lung disease.
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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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On Tree-Based Phylogenetic Networks.
Zhang, Louxin
2016-07-01
A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree-based networks. We present a simple necessary and sufficient condition for tree-based networks and prove that a universal tree-based network exists for any number of taxa that contains as its base every phylogenetic tree on the same set of taxa. This answers two problems posted by Francis and Steel recently. A byproduct is a computer program for generating random binary phylogenetic networks under the uniform distribution model.
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Nanoparticle-based drug delivery systems: promising approaches against infections
International Nuclear Information System (INIS)
Ranghar, Shweta; Sirohi, Parul; Verma, Pritam; Agarwal, Vishnu
2014-01-01
Despite the fact that many new drugs and technologies have been developed to combat the infectious diseases, these have continued to be global health challenges. The use of conventional antimicrobial agents against these infections is always associated with problems such as the development of multiple drug resistance and adverse side effects. In addition, the inefficient traditional drug delivery system results in inadequate therapeutic index, low bioavailability of drugs and many other limitations. In this regard, antimicrobial nanoparticles and nanosized drug delivery carriers have emerged as potent effective agents against the infections. Nanoparticles have unique properties owing to their ultra small and controllable size such as high surface area, enhanced reactivity, and functionalizable structure. This review focused on different classes of antimicrobial nanoparticles, including metal, metal oxide and others along with their mechanism of action and their potential use against the infections. The review also focused on the development of nanoparticle systems for antimicrobial drug delivery and use of these systems for delivery of various antimicrobial agents, giving an overview about modern nanoparticle based therapeutic strategies against the infections. (author)
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Nanoparticle-based drug delivery systems: promising approaches against infections
Energy Technology Data Exchange (ETDEWEB)
Ranghar, Shweta; Sirohi, Parul [Department of Applied Mechanics, Motilal Nehru National Institute of Technology, Allahabad (India); Verma, Pritam; Agarwal, Vishnu [Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad (India)
2014-03-15
Despite the fact that many new drugs and technologies have been developed to combat the infectious diseases, these have continued to be global health challenges. The use of conventional antimicrobial agents against these infections is always associated with problems such as the development of multiple drug resistance and adverse side effects. In addition, the inefficient traditional drug delivery system results in inadequate therapeutic index, low bioavailability of drugs and many other limitations. In this regard, antimicrobial nanoparticles and nanosized drug delivery carriers have emerged as potent effective agents against the infections. Nanoparticles have unique properties owing to their ultra small and controllable size such as high surface area, enhanced reactivity, and functionalizable structure. This review focused on different classes of antimicrobial nanoparticles, including metal, metal oxide and others along with their mechanism of action and their potential use against the infections. The review also focused on the development of nanoparticle systems for antimicrobial drug delivery and use of these systems for delivery of various antimicrobial agents, giving an overview about modern nanoparticle based therapeutic strategies against the infections. (author)
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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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Silk Electrogel Based Gastroretentive Drug Delivery System
Wang, Qianrui
Gastric cancer has become a global pandemic and there is imperative to develop efficient therapies. Oral dosing strategy is the preferred route to deliver drugs for treating the disease. Recent studies suggested silk electro hydrogel, which is pH sensitive and reversible, has potential as a vehicle to deliver the drug in the stomach environment. The aim of this study is to establish in vitro electrogelation e-gel based silk gel as a gastroretentive drug delivery system. We successfully extended the duration of silk e-gel in artificial gastric juice by mixing silk solution with glycerol at different ratios before the electrogelation. Structural analysis indicated the extended duration was due to the change of beta sheet content. The glycerol mixed silk e-gel had good doxorubicin loading capability and could release doxorubicin in a sustained-release profile. Doxorubicin loaded silk e-gels were applied to human gastric cancer cells. Significant cell viability decrease was observed. We believe that with further characterization as well as functional analysis, the silk e-gel system has the potential to become an effective vehicle for gastric drug delivery applications.
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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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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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Spatial spreading of infectious disease via local and national mobility networks in South Korea
Kwon, Okyu; Son, Woo-Sik
2017-12-01
We study the spread of infectious disease based on local- and national-scale mobility networks. We construct a local mobility network using data on urban bus services to estimate local-scale movement of people. We also construct a national mobility network from orientation-destination data of vehicular traffic between highway tollgates to evaluate national-scale movement of people. A metapopulation model is used to simulate the spread of epidemics. Thus, the number of infected people is simulated using a susceptible-infectious-recovered (SIR) model within the administrative division, and inter-division spread of infected people is determined through local and national mobility networks. In this paper, we consider two scenarios for epidemic spread. In the first, the infectious disease only spreads through local-scale movement of people, that is, the local mobility network. In the second, it spreads via both local and national mobility networks. For the former, the simulation results show infected people sequentially spread to neighboring divisions. Yet for the latter, we observe a faster spreading pattern to distant divisions. Thus, we confirm the national mobility network enhances synchronization among the incidence profiles of all administrative divisions.
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Aptamers as Both Drugs and Drug-Carriers
Directory of Open Access Journals (Sweden)
Md. Ashrafuzzaman
2014-01-01
Full Text Available Aptamers are short nucleic acid oligos. They may serve as both drugs and drug-carriers. Their use as diagnostic tools is also evident. They can be generated using various experimental, theoretical, and computational techniques. The systematic evolution of ligands by exponential enrichment which uses iterative screening of nucleic acid libraries is a popular experimental technique. Theory inspired methodology entropy-based seed-and-grow strategy that designs aptamer templates to bind specifically to targets is another one. Aptamers are predicted to be highly useful in producing general drugs and theranostic drugs occasionally for certain diseases like cancer, Alzheimer’s disease, and so on. They bind to various targets like lipids, nucleic acids, proteins, small organic compounds, and even entire organisms. Aptamers may also serve as drug-carriers or nanoparticles helping drugs to get released in specific target regions. Due to better target specific physical binding properties aptamers cause less off-target toxicity effects. Therefore, search for aptamer based drugs, drug-carriers, and even diagnostic tools is expanding fast. The biophysical properties in relation to the target specific binding phenomena of aptamers, energetics behind the aptamer transport of drugs, and the consequent biological implications will be discussed. This review will open up avenues leading to novel drug discovery and drug delivery.
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Adverse event management in mass drug administration for neglected tropical diseases.
Caplan, Arthur; Zink, Amanda
2014-03-01
The ethical challenges of reporting and managing adverse events (AEs) and serious AEs (SAEs) in the context of mass drug administration (MDA) for the treatment of neglected tropical diseases (NTDs) require reassessment of domestic and international policies on a global scale. Although the World Health Organization has set forth AE/SAE guidelines specifically for NTD MDA that incorporate suspected causality, and recommends that only SAEs get reported in this setting, most regulatory agencies continue to require the reporting of all SAEs exhibiting even a merely temporal relationship to activities associated with an MDA program. This greatly increases the potential for excess "noise" and undue risk aversion and is not only impractical but arguably unethical where huge proportions of populations are being treated for devastating diseases, and no good baseline exists against which to compare possible AE/SAE reports. Other population-specific variables that might change the way drug safety ought to be assessed include differing efficacy rates of a drug, background morbidity/mortality rates of the target disease in question, the growth rate of the incidence of disease, the availability of rescue or salvage therapies, and the willingness of local populations to take risks that other populations might not. The fact that NTDs are controllable and potentially eradicable with well-tolerated, effective, existing drugs might further alter our assessment of MDA safety and AE/SAE tolerability. At the same time, diffuseness of population, communication barriers, lack of resources, and other difficult surveillance challenges may present in NTD-affected settings. These limitations could impair the ability to monitor an MDA program's success, as well as hinder efforts to obtain informed consent or provide rescue therapy. Denying beneficial research interventions and MDA programs intended to benefit millions requires sound ethical justification based on more than the identification of
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FOCUS ON MAGNESIUM BASED DRUGS
Directory of Open Access Journals (Sweden)
I. I. Esenova
2011-01-01
Full Text Available Magnesium deficiency in the organism is one of the most common human deficiency states. The prevalence of magnesium deficiency is about 15%, and suboptimal magnesium level is observed more than in 30% of people in the general population. Clinical signs of hypomagnesaemia are observed in 40% of patients in general care hospitals, in 70% of patients - in intensive care units, and magnesium deficiency occurs in 90% of patients with acute coronary syndrome. Magnesium metabolic disorders in the organism accelerate significantly development of complications of coronary heart disease, hypertension, type 2 diabetes, asthma and a number of neurological and psychiatric diseases. The value of this macro in the body is well studied, and its daily need is identified depending on age and sex. It is known that magnesium intake with the food does not cover an organism need. It is a rationale for preventive and therapeutic use of magnesium based drugs in various diseases. Organic salts of magnesium are recommended for these purposes. Magnesium metabolic disorders, approaches to pharmacotherapeutic correction of magnesium deficiency, advantages of magnesium salts of orotic acid are reviewed.
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Stortecky, Stefan; Stefanini, Giulio G; daCosta, Bruno R; Rutjes, Anne Wilhelmina; Di Nisio, Marcello; Siletta, Maria G; Maione, Ausilia; Alfonso, Fernando; Clemmensen, Peter M; Collet, Jean-Philippe; Cremer, Jochen; Falk, Volkmar; Filippatos, Gerasimos; Hamm, Christian; Head, Stuart; Kappetein, Arie Pieter; Kastrati, Adnan; Knuuti, Juhani; Landmesser, Ulf; Laufer, Günther; Neumann, Franz-Joseph; Richter, Dimitri; Schauerte, Patrick; Sousa Uva, Miguel; Taggart, David P; Torracca, Lucia; Valgimigli, Marco; Wijns, William; Witkowski, Adam; Kolh, Philippe; Juni, Peter
2014-01-01
Objective To investigate whether revascularisation improves prognosis compared with medical treatment among patients with stable coronary artery disease. Design Bayesian network meta-analyses to combine direct within trial comparisons between treatments with indirect evidence from other trials while maintaining randomisation. Eligibility criteria for selecting studies A strategy of initial medical treatment compared with revascularisation by coronary artery bypass grafting or Food and Drug Administration approved techniques for percutaneous revascularization: balloon angioplasty, bare metal stent, early generation paclitaxel eluting stent, sirolimus eluting stent, and zotarolimus eluting (Endeavor) stent, and new generation everolimus eluting stent, and zotarolimus eluting (Resolute) stent among patients with stable coronary artery disease. Data sources Medline and Embase from 1980 to 2013 for randomised trials comparing medical treatment with revascularisation. Main outcome measure All cause mortality. Results 100 trials in 93 553 patients with 262 090 patient years of follow-up were included. Coronary artery bypass grafting was associated with a survival benefit (rate ratio 0.80, 95% credibility interval 0.70 to 0.91) compared with medical treatment. New generation drug eluting stents (everolimus: 0.75, 0.59 to 0.96; zotarolimus (Resolute): 0.65, 0.42 to 1.00) but not balloon angioplasty (0.85, 0.68 to 1.04), bare metal stents (0.92, 0.79 to 1.05), or early generation drug eluting stents (paclitaxel: 0.92, 0.75 to 1.12; sirolimus: 0.91, 0.75 to 1.10; zotarolimus (Endeavor): 0.88, 0.69 to 1.10) were associated with improved survival compared with medical treatment. Coronary artery bypass grafting reduced the risk of myocardial infarction compared with medical treatment (0.79, 0.63 to 0.99), and everolimus eluting stents showed a trend towards a reduced risk of myocardial infarction (0.75, 0.55 to 1.01). The risk of subsequent revascularisation was noticeably
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Dry Eye Treatment Based on Contact Lens Drug Delivery: A Review.
Guzman-Aranguez, Ana; Fonseca, Begoña; Carracedo, Gonzalo; Martin-Gil, Alba; Martinez-Aguila, Alejandro; Pintor, Jesús
2016-09-01
Dry eye disease affects a substantial segment of the word population with increasing frequency. It is a multifactorial disease of the ocular surface and tear film, which causes ocular discomfort, visual disturbances, and tear instability with potential damage to the cornea and conjunctiva. Because of its multifactorial etiology, the use of different pharmacological treatment for dry eye treatment has been proposed, which include anti-inflammatory molecules, lubricants or comfort agents, and secretagogues. However, in some cases these pharmacological approaches only relieve symptoms temporarily, and consequently, eye care professionals continue to have difficulties managing dry eye. To improve pharmacological therapy that allows a more efficient and long-term action, effective ocular drug delivery of the currently available drugs for dry eye treatment is required. Contact lenses are emerging as alternative ophthalmic drugs delivery systems that provide an increased residence time of the drug at the eye, thus leading to enhanced bioavailability and more convenient and efficacious therapy. In this article, we reviewed the different techniques used to prepare contact lens-based drug delivery systems and focused on articles that describe the delivery of compounds for dry eye treatment through contact lenses.
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Directory of Open Access Journals (Sweden)
Jing Zhao
Full Text Available Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.
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Mathematical modeling and computational prediction of cancer drug resistance.
Sun, Xiaoqiang; Hu, Bin
2017-06-23
Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of
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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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A kernel for open source drug discovery in tropical diseases.
Ortí, Leticia; Carbajo, Rodrigo J; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M; Rai, Arti K; Taylor, Ginger; Todd, Matthew H; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A
2009-01-01
Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.
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Evidence-based therapy for sleep disorders in neurodegenerative diseases
Directory of Open Access Journals (Sweden)
LIU Ling
2013-08-01
Full Text Available Objective To evaluate the effectiveness of the treatments for sleep disorders in neurodegenerative diseases so as to provide the best therapeutic regimens for the evidence-based treatment. Methods Search PubMed, MEDLINE, Cochrane Library, Wanfang Data and China National Knowledge Infrastructure (CNKI databases with "sleep disorder or sleep disturbance", "neurodegenerative diseases", "Parkinson's disease or PD", "Alzheimer's disease or AD", "multiple system atrophy or MSA" as retrieval words. The quality of the articles were evaluated with Jadad Scale. Results A total of 35 articles, including 2 systematic reviews, 5 randomized controlled trials, 13 clinical controlled trials, 13 case series and 2 epidemiological investigation studies were included for evaluation, 13 of which were high grade and 22 were low grade articles. Clinical evidences showed that: 1 advice on sleep hygiene, careful use of dopaminergic drugs and hypnotic sedative agents should be considered for PD. Bright light therapy (BLT may improve circadian rhythm sleep disorders and clonazepam may be effective for rapid eye movement sleep behavior disorder (RBD. However, to date, very few controlled studies are available to make a recommendation for the management of sleep disorders in PD; 2 treatments for sleep disorders in AD include drug therapy (e.g. melatonin, acetylcholinesterase inhibitors, antipsychotic drugs, antidepressants and non-drug therapy (e.g. BLT, behavior therapy, but very limited evidence shows the effectiveness of these treatments; 3 the first line treatment for sleep-related breathing disorder in MSA is nasal continuous positive airway pressure (nCPAP, and clonazepam is effective for RBD in MSA; 4 there is rare evidence related to the treatment of sleep disorders in dementia with Lewy body (DLB and amyotrophic lateral sclerosis (ALS. Conclusion Evidence-based medicine can provide the best clinical evidence on sleep disorders' treatment in neurodegenerative
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Biomaterial-based drug delivery systems for the controlled release of neurotrophic factors
International Nuclear Information System (INIS)
Mohtaram, Nima Khadem; Montgomery, Amy; Willerth, Stephanie M
2013-01-01
This review highlights recent work on the use of biomaterial-based drug delivery systems to control the release of neurotrophic factors as a potential strategy for the treatment of neurological disorders. Examples of neurotrophic factors include the nerve growth factor, the glial cell line-derived neurotrophic factor, the brain-derived neurotrophic factor and neurotrophin-3. In particular, this review focuses on two methods of drug delivery: affinity-based and reservoir-based systems. We review the advantages and challenges associated with both types of drug delivery system and how these systems can be applied to neurological diseases and disorders. While a limited number of affinity-based delivery systems have been developed for the delivery of neurotrophic factors, we also examine the broad spectrum of reservoir-based delivery systems, including microspheres, electrospun nanofibers, hydrogels and combinations of these systems. Finally, conclusions are drawn about the current state of such drug delivery systems as applied to neural tissue engineering along with some thoughts on the future direction of the field. (topical review)
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Wang, Meng-Ting; Wang, Yun-Han; Chang, Hsin-An; Tsai, Chen-Liang; Yang, Ya-Sung; Lin, Chen Wei; Kuo, Cheng-Chin; Hsu, Yu-Juei
2017-01-01
Concerns were raised about pneumonia development from benzodiazepines (BZDs) and Z-drugs, but direct evidence is limited, conflicting and without examining the highly susceptible patients with chronic kidney disease (CKD) nor specifying the risk for different drug utilizations. This study aimed to investigate whether use of BZDs and Z-drugs was each associated with an increased risk of pneumonia in a CKD population. We performed a nested case-control study of 36,880 CKD patients analyzing the Taiwan National Health Insurance Database between 01/1/2000 and 12/31/2011. Among the study cohort, we identified 4,533 cases of pneumonia based on validated disease codes, chest x-ray examination, and prescriptions of respiratory antibiotics, and randomly selected 16,388 controls from risk sets, matched by sex, age, and number of CKD-related hospitalizations. All prescription filling records of BZDs and Z-drugs in the year before the event/index date were analyzed for cases and controls. Conditional logistic regressions were performed to estimate the odds ratios (ORs). Current use of BZDs was associated with a 1.31-fold (95% CI, 1.18-1.26) increased risk of pneumonia compared to nonuse, but not for recent and past use. The risk from current BZD use was confined to new initiation (adjusted OR, 2.47; 95% CI, 2.02-3.03) or use for ≤ 30 days, and elevated to 2.88-fold (95% CI, 1.87-4.42) with parenteral administration. New initiation and current short-term use of Z-drugs was associated with a 2.94-fold (95% CI, 1.65-5.26) and 1.75-fold (95% CI, 1.13-2.72) increased risk of pneumonia, respectively. The findings were robust to adoption of a case-crossover study that analyzed cases only. Use of BZRAs is associated with an increased risk of pneumonia in CKD patients, especially for patients newly initiating BZDs or Z-drugs or those injected with BZDs. Physicians should exercise cautions for signs of pneumonia when prescribing BZDs or Z-drugs to CKD patients.
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Dehghan-Niri, Maryam; Tavakol, Moslem; Vasheghani-Farahani, Ebrahim; Ganji, Fariba
2015-05-01
In the present study, injectable hydrogels based on gum tragacanth-tyramine conjugate were prepared by enzymatic oxidation of tyramine radicals in the presence of hydrogen peroxide. Then, in vitro release of bovine serum albumin and insulin as model protein drugs from this polymeric network was investigated. Also, to improve the properties of this hydrogel, a blended hydrogel composed of tyramine-conjugated gelatin and tyramine-conjugated tragacanth was prepared. Experimental results showed that the gelation time ranged from 3 to 28 s depending on the polymer and enzyme concentrations. Results of morphological investigation of hydrogels indicated that the average pore size of hydrogels varied from 120 to 160 µm. Swelling degree of hydrogels and the rate of drug release decreased by increasing of hydrogen peroxide and polymer concentrations. The release profile of drug from hydrogels followed Higuchi and Fickian diffusion mechanism. Finally, it was shown that the swelling characteristics and drug release behavior of this polymeric network could be improved by blending it with tyramine-conjugated gelatin. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
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Affordability Challenges to Value-Based Pricing: Mass Diseases, Orphan Diseases, and Cures.
Danzon, Patricia M
2018-03-01
To analyze how value-based pricing (VBP), which grounds the price paid for pharmaceuticals in their value, can manage "affordability" challenges, defined as drugs that meet cost-effectiveness thresholds but are "unaffordable" within the short-run budget. Three specific contexts are examined, drawing on recent experience. First, an effective new treatment for a chronic, progressive disease, such as hepatitis C, creates a budget spike that is transitory because initial prevalence is high, relative to current incidence. Second, "cures" that potentially provide lifetime benefits may claim abnormally high VBP prices, with high immediate budget impact potentially/partially offset by deferred cost savings. Third, although orphan drugs in principle target rare diseases, in aggregate they pose affordability concerns because of the growing number of orphan indications and increasingly high prices. For mass diseases, the transitory budget impact of treating the accumulated patient stock can be managed by stratified rollout that delays treatment of stable patients and prioritizes patients at high risk of deterioration. Delay spreads the budget impact and permits potential savings from launch of competing treatments. For cures, installment payments contingent on outcomes could align payment flows and appropriately shift risk to producers. This approach, however, entails high administrative and incentive costs, especially if applied across multiple payers in the United States. For orphan drugs, the available evidence on research and development trends and returns argues against the need for a higher VBP threshold to incentivize research and development in orphan drugs, given existing statutory benefits under orphan drug legislation. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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Sengur, Abdulkadir; Akbulut, Yaman; Guo, Yanhui; Bajaj, Varun
2017-12-01
Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features. In it, Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The CNN architecture is trained with the reinforcement sample learning strategy. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.80% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.
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Nonsteroidal anti-inflammatory drugs and the risk of Parkinson disease.
Bornebroek, Marjolijn; de Lau, Lonneke M L; Haag, Mendel D M; Koudstaal, Peter J; Hofman, Albert; Stricker, Bruno H C; Breteler, Monique M B
2007-01-01
Several lines of evidence suggest a role of inflammatory processes in Parkinson disease, although it is still unclear whether inflammation is a cause or rather a consequence of neurodegeneration. In a prospective population-based cohort study among 6,512 participants aged >or=55 years, with repeated in-person examination, we evaluated the association between cumulative use of nonsteroidal anti-inflammatory drugs (NSAIDs) and the risk of Parkinson disease. Complete information on filled prescriptions was available from automated pharmacy records. Data were analyzed by means of Cox proportional hazards regression analysis, adjusted for age, sex, smoking habits and coffee consumption. After an average 9.4 years of follow-up, 88 new cases of Parkinson disease were detected. No association was found between use of NSAIDs and the risk of Parkinson disease (adjusted hazard ratio for any NSAID use, 1.50; 95% confidence interval, 0.95-2.37). Our findings do not support the hypothesis that NSAIDs might decrease the risk of Parkinson disease. (c) 2007 S. Karger AG, Basel.
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Directory of Open Access Journals (Sweden)
Warren D Anderson
2017-07-01
Full Text Available Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension. We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.
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What does systems biology mean for drug development?
Schrattenholz, André; Soskić, Vukić
2008-01-01
The complexity and flexibility of cellular architectures is increasingly recognized by impressive progress on the side of molecular analytics, i.e. proteomics, genomics and metabolomics. One of the messages from systems biology is that the number of molecular species in cellular networks is orders of magnitude bigger than anticipated by genomic analysis, in particular by fast posttranslational modifications of proteins. The requirements to manage external signals, integrate spatiotemporal signal transduction inside an organism and at the same time optimizing networks of biochemical and chemical reactions result in chemically extremely fine tuned molecular entities. Chemical side reactions of enzymatic activity, like e.g. random oxidative damage of proteins by free radicals during aging constantly introduce epigenetic alterations of protein targets. These events gradually and on an individual stochastic scale, keep modifying activities of these targets, and their affinities and selectivities towards biological and pharmacological ligands. One further message is that many of the key reactions in living systems are essentially based on interactions of low affinities and even low selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. So, in complex disorders like cancer or neurodegenerative diseases, which are rooted in relatively subtle and multimodal dysfunction of important physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which still dominate the pharmaceutical industry increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade and the treatment of "complex diseases" remains a most pressing medical need. Currently a change of paradigm can be observed with
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Confidence from uncertainty - A multi-target drug screening method from robust control theory
Directory of Open Access Journals (Sweden)
Petzold Linda R
2010-11-01
Full Text Available Abstract Background Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty. Results We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty. Conclusions The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.
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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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Froestl, Wolfgang; Pfeifer, Andrea; Muhs, Andreas
2013-01-01
Cognitive enhancers (nootropics) are drugs to treat cognition deficits in patients suffering from Alzheimer's disease, schizophrenia, stroke, attention deficit hyperactivity disorder, or aging. Cognition refers to a capacity for information processing, applying knowledge, and changing preferences. It involves memory, attention, executive functions, perception, language, and psychomotor functions. The term nootropics was coined in 1972 when memory enhancing properties of piracetam were observed in clinical trials. In the meantime, hundreds of drugs have been evaluated in clinical trials or in preclinical experiments. To classify the compounds, a concept is proposed assigning drugs to 19 categories according to their mechanism(s) of action, in particular drugs interacting with receptors, enzymes, ion channels, nerve growth factors, re-uptake transporters, antioxidants, metal chelators, and disease modifying drugs, meaning small molecules, vaccines, and monoclonal antibodies interacting with amyloid-β and tau. For drugs, whose mechanism of action is not known, they are either classified according to structure, e.g., peptides, or their origin, e.g., natural products. The review covers the evolution of research in this field over the last 25 years.
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Adverse drug reaction reports for cardiometabolic drugs from sub Sahara Africa: A study in VigiBase
Berhe, Derbew F.; Juhlin, Kristina; Star, Kristina; Haaijer-Ruskamp, Flora M.; Michael, Kidane; Taxis, Katja; Mol, Peter G. M.
2014-01-01
Background: Many pharmacovigilance centers have been established in Sub Sahara Africa (SSA) in recent years. Their focus has been on ADRs to drugs for communicable diseases. Little is known about ADRs caused by drugs for cardiometabolic diseases, although its burden is increasing rapidly in SSA.
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Needle, Richard; Kroeger, Karen; Belani, Hrishikesh; Achrekar, Angeli; Parry, Charles D; Dewing, Sarah
2008-11-01
South Africa is experiencing significant changes in patterns of illicit drug use, including increasing injection and non-injection drug use, and the use of drugs by persons engaged in sex work, both of which could further expand the HIV/AIDS epidemic. In 2005, a rapid ethnographic assessment was conducted in Durban, South Africa, to learn more about patterns of drug use and HIV risk behaviors among drug-using, street-based sex workers. Field teams recruited 52 current injection and non-injection drug users for key informant interviews and focus groups, and they conducted mapping and observation in identified high-risk neighborhoods. Key informants were offered free, voluntary counseling and HIV rapid testing. The results of the assessment indicate that in this population, drugs play an organizing role in patterns of daily activities, with sex work closely linked to the buying, selling, and using of drugs. Participants reported using multiple drugs including crack cocaine, heroin, Ecstasy and Mandrax, and their choices were based on their expectations about the functional role and behavioral and pharmacological properties of the drugs. The organization of sex work and patterns of drug use differ by gender, with males exercising more control over daily routines and drug and sexual transactions than females. Activities of female sex workers are subject to considerable control by individual pimps, many of whom also function as landlords and drug dealers. A strong hold over the overlapping economies of drugs and sex work by a few individuals extends to control of the physical and social settings in which sex is exchanged and drugs are sold and used as well as the terms under which sex work is carried out. The potential for accelerated HIV spread is considerable given the evidence of overlapping drug-using and sexual risk behaviors and the mixing patterns across drug and sexual risk networks.
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Silk Fibroin-Based Nanoparticles for Drug Delivery
Zhao, Zheng; Li, Yi; Xie, Mao-Bin
2015-01-01
Silk fibroin (SF) is a protein-based biomacromolecule with excellent biocompatibility, biodegradability and low immunogenicity. The development of SF-based nanoparticles for drug delivery have received considerable attention due to high binding capacity for various drugs, controlled drug release properties and mild preparation conditions. By adjusting the particle size, the chemical structure and properties, the modified or recombinant SF-based nanoparticles can be designed to improve the therapeutic efficiency of drugs encapsulated into these nanoparticles. Therefore, they can be used to deliver small molecule drugs (e.g., anti-cancer drugs), protein and growth factor drugs, gene drugs, etc. This paper reviews recent progress on SF-based nanoparticles, including chemical structure, properties, and preparation methods. In addition, the applications of SF-based nanoparticles as carriers for therapeutic drugs are also reviewed. PMID:25749470
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Directory of Open Access Journals (Sweden)
Yong-Yeol Ahn
Full Text Available The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.
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Non-steroidal anti-inflammatory drugs and cyclooxygenase in Alzheimer's disease
Hoozemans, Jeroen J. M.; Veerhuis, Robert; Rozemuller, Annemieke J. M.; Eikelenboom, Piet
2003-01-01
Epidemiological studies indicate that anti-inflammatory drugs, especially the non-steroidal anti-inflammatory drugs (NSAIDs), decrease the risk of developing Alzheimer's disease (AD). Their beneficial effects may be due to interference in the chronic inflammatory reaction, that takes place in AD.
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Drug delivery matrices based on scleroglucan/alginate/borax gels.
Matricardi, Pietro; Onorati, Ilenia; Coviello, Tommasina; Alhaique, Franco
2006-06-19
The aim of this work is to obtain a new drug delivery matrix, especially designed for protein delivery, based on biodegradable and biocompatible polymers, and to describe its main physico-chemical properties. A polysaccharide based semi-interpenetrating polymer network (semi-IPN) was built up, composed by sodium alginate chains interspersed into a scleroglucan/borax hydrogel network. Tablets were obtained by compression of the resulting freeze-dried hydrogel. The different release and physico-chemical properties possessed by the two starting polymers in various aqueous media were combined in the new matrix. In this work, description is given of the in vitro ability of the matrix to deliver in a controlled manner a protein, Myoglobin, in distilled water, simulated gastric fluid and simulated intestinal fluid; the release, simulating a gastric passage, followed by an enteric delivery, was also carried out. Water uptake data, colorimetric experiments and scanning electron microscopy images are given for the characterization of this new solid dosage form; the importance of the borax presence is also discussed.
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Meningococcal disease and future drug targets
DEFF Research Database (Denmark)
Gammelgaard, L K; Colding, H; Hartzen, S H
2011-01-01
recent data and current knowledge on molecular mechanisms of meningococcal disease and explains how host immune responses ultimately may aggravate neuropathology and the clinical prognosis. Within this context, particular importance is paid to the endotoxic components that provide potential drug targets...... for novel neuroprotective adjuvants, which are needed in order to improve the clinical management of meningoencephalitis and patient prognosis....
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Pivette, Mathilde; Mueller, Judith E; Crépey, Pascal; Bar-Hen, Avner
2014-11-18
This systematic literature review aimed to summarize evidence for the added value of drug sales data analysis for the surveillance of infectious diseases. A search for relevant publications was conducted in Pubmed, Embase, Scopus, Cochrane Library, African Index Medicus and Lilacs databases. Retrieved studies were evaluated in terms of objectives, diseases studied, data sources, methodologies and performance for real-time surveillance. Most studies compared drug sales data to reference surveillance data using correlation measurements or indicators of outbreak detection performance (sensitivity, specificity, timeliness of the detection). We screened 3266 articles and included 27 in the review. Most studies focused on acute respiratory and gastroenteritis infections. Nineteen studies retrospectively compared drug sales data to reference clinical data, and significant correlations were observed in 17 of them. Four studies found that over-the-counter drug sales preceded clinical data in terms of incidence increase. Five studies developed and evaluated statistical algorithms for selecting drug groups to monitor specific diseases. Another three studies developed models to predict incidence increase from drug sales. Drug sales data analyses appear to be a useful tool for surveillance of gastrointestinal and respiratory disease, and OTC drugs have the potential for early outbreak detection. Their utility remains to be investigated for other diseases, in particular those poorly surveyed.
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Froestl, Wolfgang; Pfeifer, Andrea; Muhs, Andreas
2014-01-01
Scientists working in the field of Alzheimer's disease and, in particular, cognitive enhancers, are very productive. The review "Drugs interacting with Targets other than Receptors or Enzymes. Disease-modifying Drugs" was accepted in October 2012. In the last 20 months, new targets for the potential treatment of Alzheimer's disease were identified. Enormous progress was realized in the pharmacological characterization of natural products with cognitive enhancing properties. This review covers the evolution of research in this field through May 2014.
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AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.
Directory of Open Access Journals (Sweden)
Jiansong Fang
Full Text Available Alzheimer's disease (AD is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs to delay disease progression. The in silico prediction of chemical-protein interactions (CPI can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB and recursive partitioning (RP algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.
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Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
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Epidemiologic Considerations in Network Modeling of Theoretical Disease Events
National Research Council Canada - National Science Library
Lem, Marcus
2006-01-01
.... Network analysis has shown utility in the study of a range of communicable disease outbreaks affecting both health and commerce, including SARS, tuberculosis, syphilis and foot-and mouth-disease...
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Disease spreading with epidemic alert on small-world networks
International Nuclear Information System (INIS)
Han, Xiao-Pu
2007-01-01
Base on two-dimension small-world networks, a susceptible-infected model with epidemic alert is proposed in this Letter. In this model, if some parts of the network are alarmed as dangerous, a fraction of edges between the alarmed parts and others will be removed, and two cases of alerting rules that the degree and frequency of contacts kept unchanged are considered respectively. The numerical simulations show that the spreading velocity is reduced by the accurate and timely epidemic alert, and the more accurate and timely, the stronger the deceleration effect. This model indicates that to broadcast epidemic alert timely is helpful and necessary in the control of epidemic spreading, and in agreement with the general view of epidemic alert. This work is helpful to understand the effects of epidemic alert on disease spreading
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Kumar, Akhil; Tiwari, Ashish; Sharma, Ashok
2018-03-15
Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis is not able to provide complete solution of AD due to multifactorial nature of disease and one target one drug seems to fail to provide better treatment against AD. Moreover, current available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So the current AD drug discovery research shifting towards new approach for better solution that simultaneously modulate more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs. Drug discovery project is tedious, costly and long term project. Moreover, multi target AD drug discovery added extra challenges such as good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off target side effect and crossing of the blood brain barrier. These hurdles may be addressed by insilico methods for efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here we are summarizing some of the most prominent and computationally explored single target against AD and further we discussed successful example of dual or multiple inhibitors for same targets. Moreover we focused on ligand and structure based computational approach to design MTDL against AD. However is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy are useful in future MTDLs drug discovery alone or in combination with fragment based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug
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Directory of Open Access Journals (Sweden)
Xinghuan Wang
Full Text Available Lower urinary tract symptoms (LUTS due to benign prostatic hyperplasia (BPH are common in elder men and a number of drugs alone or combined are clinically used for this disorder. But available studies investigating the comparative effects of different drug therapies are limited. This study was aimed to compare the efficacy of different drug therapies for LUTS/BPH with network meta-analysis.An electronic search of PubMed, Cochrane Library and Embase was performed to identify randomized controlled trials (RCTs comparing different drug therapies for LUTS/BPH within 24 weeks. Comparative effects were calculated using Aggregate Data Drug Information System. Consistency models of network meta-analysis were created and cumulative probability was used to rank different therapies.A total 66 RCTs covering seven different therapies with 29384 participants were included. We found that α-blockers (ABs plus phosphodiesterase 5 inhibitors (PDE5-Is ranked highest in the test of IPSS total score, storage subscore and voiding subscore. The combination therapy of ABs plus 5α-reductase inhibitors was the best for increasing maximum urinary flow rate (Qmax with a mean difference (MD of 1.98 (95% CI, 1.12 to 2.86 as compared to placebo. ABs plus muscarinic receptor antagonists (MRAs ranked secondly on the reduction of IPSS storage subscore, although monotherapies including MRAs showed no effect on this aspect. Additionally, PDE5-Is alone showed great effectiveness for LUTS/BPH except Qmax.Based on our novel findings, combination therapy, especially ABs plus PDE5-Is, is recommended for short-term treatment for LUTS/BPH. There was also evidence that PDE5-Is used alone was efficacious except on Qmax. Additionally, it should be cautious when using MRAs. However, further clinical studies are required for longer duration which considers more treatment outcomes such as disease progression, as well as basic research investigating mechanisms involving PDE5-Is and other
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Directory of Open Access Journals (Sweden)
Hanneke de Waal
Full Text Available Synaptic loss is a major hallmark of Alzheimer's disease (AD. Disturbed organisation of large-scale functional brain networks in AD might reflect synaptic loss and disrupted neuronal communication. The medical food Souvenaid, containing the specific nutrient combination Fortasyn Connect, is designed to enhance synapse formation and function and has been shown to improve memory performance in patients with mild AD in two randomised controlled trials.To explore the effect of Souvenaid compared to control product on brain activity-based networks, as a derivative of underlying synaptic function, in patients with mild AD.A 24-week randomised, controlled, double-blind, parallel-group, multi-country study.179 drug-naïve mild AD patients who participated in the Souvenir II study.Patients were randomised 1∶1 to receive Souvenaid or an iso-caloric control product once daily for 24 weeks.In a secondary analysis of the Souvenir II study, electroencephalography (EEG brain networks were constructed and graph theory was used to quantify complex brain structure. Local brain network connectivity (normalised clustering coefficient gamma and global network integration (normalised characteristic path length lambda were compared between study groups, and related to memory performance.THE NETWORK MEASURES IN THE BETA BAND WERE SIGNIFICANTLY DIFFERENT BETWEEN GROUPS: they decreased in the control group, but remained relatively unchanged in the active group. No consistent relationship was found between these network measures and memory performance.The current results suggest that Souvenaid preserves the organisation of brain networks in patients with mild AD within 24 weeks, hypothetically counteracting the progressive network disruption over time in AD. The results strengthen the hypothesis that Souvenaid affects synaptic integrity and function. Secondly, we conclude that advanced EEG analysis, using the mathematical framework of graph theory, is useful and
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de Waal, Hanneke; Stam, Cornelis J; Lansbergen, Marieke M; Wieggers, Rico L; Kamphuis, Patrick J G H; Scheltens, Philip; Maestú, Fernando; van Straaten, Elisabeth C W
2014-01-01
Synaptic loss is a major hallmark of Alzheimer's disease (AD). Disturbed organisation of large-scale functional brain networks in AD might reflect synaptic loss and disrupted neuronal communication. The medical food Souvenaid, containing the specific nutrient combination Fortasyn Connect, is designed to enhance synapse formation and function and has been shown to improve memory performance in patients with mild AD in two randomised controlled trials. To explore the effect of Souvenaid compared to control product on brain activity-based networks, as a derivative of underlying synaptic function, in patients with mild AD. A 24-week randomised, controlled, double-blind, parallel-group, multi-country study. 179 drug-naïve mild AD patients who participated in the Souvenir II study. Patients were randomised 1∶1 to receive Souvenaid or an iso-caloric control product once daily for 24 weeks. In a secondary analysis of the Souvenir II study, electroencephalography (EEG) brain networks were constructed and graph theory was used to quantify complex brain structure. Local brain network connectivity (normalised clustering coefficient gamma) and global network integration (normalised characteristic path length lambda) were compared between study groups, and related to memory performance. THE NETWORK MEASURES IN THE BETA BAND WERE SIGNIFICANTLY DIFFERENT BETWEEN GROUPS: they decreased in the control group, but remained relatively unchanged in the active group. No consistent relationship was found between these network measures and memory performance. The current results suggest that Souvenaid preserves the organisation of brain networks in patients with mild AD within 24 weeks, hypothetically counteracting the progressive network disruption over time in AD. The results strengthen the hypothesis that Souvenaid affects synaptic integrity and function. Secondly, we conclude that advanced EEG analysis, using the mathematical framework of graph theory, is useful and feasible for
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Cresswell, Kathrin M; Sheikh, Aziz
2008-05-01
There is increasing interest internationally in ways of reducing the high disease burden resulting from errors in medicine management. Repeat exposure to drugs to which patients have a known allergy has been a repeatedly identified error, often with disastrous consequences. Drug allergies are immunologically mediated reactions that are characterized by specificity and recurrence on reexposure. These repeat reactions should therefore be preventable. We argue that there is insufficient attention being paid to studying and implementing system-based approaches to reducing the risk of such accidental reexposure. Drawing on recent and ongoing research, we discuss a number of information technology-based interventions that can be used to reduce the risk of recurrent exposure. Proven to be effective in this respect are interventions that provide real-time clinical decision support; also promising are interventions aiming to enhance patient recognition, such as bar coding, radiofrequency identification, and biometric technologies.
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Remission of Grave's disease after oral anti-thyroid drug treatment.
Ishtiaq, Osama; Waseem, Sabiha; Haque, M Naeemul; Islam, Najmul; Jabbar, Abdul
2009-11-01
To evaluate remission rate of anti-thyroid drug treatment in patients with Grave's disease, and to study the factors associated with remission. A cross sectional study. The Endocrine Department of the Aga Khan University Hospital, Karachi from 1999 to 2000. Seventy four patients of Grave's disease were recruited who were prescribed medical treatment. Grave's disease was diagnosed in the presence of clinical and biochemical hyperthyroidism along with anti-microsomal (AMA) and anti-thyroglobulin antibodies (ATA) and thyroid scan. These patients were prescribed oral anti-thyroid drugs using titration regime and followed at 3, 6, 12 and 18 months. Patients were categorized into two groups: "remission group" and "treatment failure group" and results were compared using a chi-square test, t-test and logistic regression model with significance at p disease on initial presentation.
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Consensus Statement on Research Definitions for Drug-Resistant Tuberculosis in Children.
Seddon, James A; Perez-Velez, Carlos M; Schaaf, H Simon; Furin, Jennifer J; Marais, Ben J; Tebruegge, Marc; Detjen, Anne; Hesseling, Anneke C; Shah, Sarita; Adams, Lisa V; Starke, Jeffrey R; Swaminathan, Soumya; Becerra, Mercedes C
2013-06-01
Few children with drug-resistant (DR) tuberculosis (TB) are identified, diagnosed, and given an appropriate treatment. The few studies that have described this vulnerable population have used inconsistent definitions. The World Health Organization (WHO) definitions used for adults with DR-TB and for children with drug-susceptible TB are not always appropriate for children with DR-TB. The Sentinel Project on Pediatric Drug-Resistant Tuberculosis was formed in 2011 as a network of experts and stakeholders in childhood DR-TB. An early priority was to establish standardized definitions for key parameters in order to facilitate study comparisons and the development of an evidence base to guide future clinical management. This consensus statement proposes standardized definitions to be used in research. In particular, it suggests consistent terminology, as well as definitions for measures of exposure, drug resistance testing, previous episodes and treatment, certainty of diagnosis, site and severity of disease, adverse events, and treatment outcome. © The Author 2013. Published by Oxford University Press on behalf of the Pediatric Infectious Diseases Society.
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Worley, Matthew J; Trim, Ryan S; Tate, Susan R; Roesch, Scott C; Myers, Mark G; Brown, Sandra A
2014-12-01
Proximal personal and environmental factors typically predict outcomes of treatment for alcohol or drug dependence (AODD), but longitudinal treatment studies have rarely examined these factors in adults with co-occurring psychiatric disorders. In adults with AODD and major depression, the aims of this study were to: (a) disaggregate person-and time-level components of network substance use and self-efficacy, (b) examine their prospective effects on posttreatment alcohol/drug use, and (c) examine whether residential environment moderated relations between these proximal factors and substance use outcomes. Veterans (N = 201) enrolled in a trial of group psychotherapy for AODD and independent MDD completed assessments every 3 months during 1 year of posttreatment follow-up. Outcome variables were percent days drinking (PDD) and using drugs (PDDRG). Proximal variables included abstinence self-efficacy and social network drinking and drug use. Self-efficacy and network substance use at the person-level prospectively predicted PDD (ps effects of social networks predicted future PDD (ps social network effects (ps network and posttreatment drinking and drug use. Both individual differences and time-specific fluctuations in proximal targets of psychosocial interventions are related to posttreatment substance use in adults with co-occurring AODD and MDD. More structured environmental settings appear to alleviate risk associated with social network substance use, and may be especially advised for those who have greater difficulty altering social networks during outpatient treatment.
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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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Morel, Carlos Medicis; Serruya, Suzanne Jacob; Penna, Gerson Oliveira; Guimarães, Reinaldo
2009-01-01
Background New approaches and tools were needed to support the strategic planning, implementation and management of a Program launched by the Brazilian Government to fund research, development and capacity building on neglected tropical diseases with strong focus on the North, Northeast and Center-West regions of the country where these diseases are prevalent. Methodology/Principal Findings Based on demographic, epidemiological and burden of disease data, seven diseases were selected by the Ministry of Health as targets of the initiative. Publications on these diseases by Brazilian researchers were retrieved from international databases, analyzed and processed with text-mining tools in order to standardize author- and institution's names and addresses. Co-authorship networks based on these publications were assembled, visualized and analyzed with social network analysis software packages. Network visualization and analysis generated new information, allowing better design and strategic planning of the Program, enabling decision makers to characterize network components by area of work, identify institutions as well as authors playing major roles as central hubs or located at critical network cut-points and readily detect authors or institutions participating in large international scientific collaborating networks. Conclusions/Significance Traditional criteria used to monitor and evaluate research proposals or R&D Programs, such as researchers' productivity and impact factor of scientific publications, are of limited value when addressing research areas of low productivity or involving institutions from endemic regions where human resources are limited. Network analysis was found to generate new and valuable information relevant to the strategic planning, implementation and monitoring of the Program. It afforded a more proactive role of the funding agencies in relation to public health and equity goals, to scientific capacity building objectives and a more
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Directory of Open Access Journals (Sweden)
Carlos Medicis Morel
Full Text Available BACKGROUND: New approaches and tools were needed to support the strategic planning, implementation and management of a Program launched by the Brazilian Government to fund research, development and capacity building on neglected tropical diseases with strong focus on the North, Northeast and Center-West regions of the country where these diseases are prevalent. METHODOLOGY/PRINCIPAL FINDINGS: Based on demographic, epidemiological and burden of disease data, seven diseases were selected by the Ministry of Health as targets of the initiative. Publications on these diseases by Brazilian researchers were retrieved from international databases, analyzed and processed with text-mining tools in order to standardize author- and institution's names and addresses. Co-authorship networks based on these publications were assembled, visualized and analyzed with social network analysis software packages. Network visualization and analysis generated new information, allowing better design and strategic planning of the Program, enabling decision makers to characterize network components by area of work, identify institutions as well as authors playing major roles as central hubs or located at critical network cut-points and readily detect authors or institutions participating in large international scientific collaborating networks. CONCLUSIONS/SIGNIFICANCE: Traditional criteria used to monitor and evaluate research proposals or R&D Programs, such as researchers' productivity and impact factor of scientific publications, are of limited value when addressing research areas of low productivity or involving institutions from endemic regions where human resources are limited. Network analysis was found to generate new and valuable information relevant to the strategic planning, implementation and monitoring of the Program. It afforded a more proactive role of the funding agencies in relation to public health and equity goals, to scientific capacity building
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Human disease MiRNA inference by combining target information based on heterogeneous manifolds.
Ding, Pingjian; Luo, Jiawei; Liang, Cheng; Xiao, Qiu; Cao, Buwen
2018-04-01
The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omics data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM. Copyright © 2018 Elsevier Inc. All rights reserved.
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Nanotechnology-Based Drug Delivery Systems for Treatment of Tuberculosis--A Review.
da Silva, Patricia Bento; de Freitas, Eduardo Sinésio; Bernegossi, Jessica; Gonçalez, Maíra Lima; Sato, Mariana Rillo; Leite, Clarice Queico Fujimura; Pavan, Fernando Rogério; Chorilli, Marlus
2016-02-01
Tuberculosis (TB) is an infectious and transmissible disease that is caused by Mycobacterium tuberculosis and primarily affects the lungs, although it can affect other organs and systems. The pulmonary presentation of TB, in addition to being more frequent, is also the most relevant to public health because it is primarily responsible for the transmission of the disease. The to their low World Health Organization (WHO) recommends a combined therapeutic regimen of several drugs, such as rifampicin (RIF), isoniazid (INH), pyrazinamide (PZA) and ethambutol (ETB). These drugs have low plasma levels after oral administration, due to their low water solubility, poor permeability and ability to be rapidly metabolized by the liver and at high concentrations. Furthermore, they have short t₁/₂ (only 1-4 hours) indicating a short residence in the plasma and the need for multiple high doses, which can result in neurotoxicity and hepatotoxicity. Nanotechnology drug delivery systems have considerable potential for the treatment of TB. The systems can also be designed to allow for the sustained release of drugs from the matrix and drug delivery to a specific target. These properties of the systems enable the improvement of the bioavailability of drugs, can reduce the dosage and frequency of administration, and may solve the problem of non-adherence to prescribed therapy, which is a major obstacle to the control of TB. The purpose of this study was to systematically review nanotechnology-based drug delivery systems for the treatment of TB.
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Directory of Open Access Journals (Sweden)
E V Pavlova
2000-01-01
Full Text Available Aim of studdy: To assess the frequency of practical application of different basic drugs in rheumatoid arthritis (RA. Material and methods: Tlxe study was conducted basing of questionner of pts and analysis of ycases by randomized sampling among 103 consequent pts (M:F= 13:90 with reliable RA (ARA, 1987 in rheumatologic department of Clinical Hospital Nol in Ekaterinburg. 74% of pts under study demonstrated systemic manifestations: anemia (in 47 pts, lymphadenopathy (in 34, rheumatoid nodules (in 15, Sjogren s syndrome (in 4, nephropathy (in 4, vascular disturbances including Raynaud s phenomenon, capillarites (by 1 pt. Results: In the course of disease basic therapy was prescribed to 88 out of103 (85.4% pts and one and the same patient could take different basic drugs. Aminochinoline drugs prevailed, after them more frequent were immunodepressants and gold preparations. More rarely pts had sulfasalazin, cuprenil and wobenzym. In general, in 133 out of 184 cases of prescribing basic drugs they were canceled. The reason for cancellation were: prevalently absence of the drug in the pharmaceutical stores (in 48 cases averagely in 8 months of taking the drug; then they insufficient efficacy (44 cases averagely in 1.3 year. In 18 cases pts themselves stopped treatment averagely in 3.5 months of drug taking. Conclusion: In the majority of cases of basic drugs cancellation in RA the cause is their absence in sail especially on free of charge prescription. Cases ofself-cancellation of the drug demonstrate the need of explaining to pts the necessity> of long-term taking disease-modifying drugs.
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ChemProt: A disease chemical biology database
DEFF Research Database (Denmark)
Taboureau, Olivier; Oprea, Tudor I.
2013-01-01
The integration of chemistry, biology, and informatics to study drug actions across multiple biological targets, pathways, and biological systems is an emerging paradigm in drug discovery. Rather than reducing a complex system to simplistic models, fields such as chemogenomics and translational...... informatics are seeking to build a holistic model for a better understanding of the drug pharmacology and clinical effects. Here we will present a webserver called ChemProt that can assist, in silico, the drug actions in the context of cellular and disease networks and contribute in the field of disease...... chemical biology, drug repurposing, and off-target effects prediction....
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e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design.
Pihan, Emilie; Colliandre, Lionel; Guichou, Jean-François; Douguet, Dominique
2012-06-01
In the drug discovery field, new uses for old drugs, selective optimization of side activities and fragment-based drug design (FBDD) have proved to be successful alternatives to high-throughput screening. e-Drug3D is a database of 3D chemical structures of drugs that provides several collections of ready-to-screen SD files of drugs and commercial drug fragments. They are natural inputs in studies dedicated to drug repurposing and FBDD. e-Drug3D collections are freely available at http://chemoinfo.ipmc.cnrs.fr/e-drug3d.html either for download or for direct in silico web-based screenings.
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Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M.; Leonardo, Cassandra D.; Jack, Clifford R.; Weiner, Michael W.; Bernstein, Matthew A.; Thompson, Paul M.
2015-01-01
Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD. PMID:26640830
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Wang, Houliang; Guo, Wenbin; Liu, Feng; Wang, Guodong; Lyu, Hailong; Wu, Renrong; Chen, Jindong; Wang, Shuai; Li, Lehua; Zhao, Jingping
2016-05-18
Increased cerebellar-default mode network (DMN) connectivity has been observed in first-episode, drug-naive patients with schizophrenia. However, it remains unclear whether increased cerebellar-DMN connectivity starts earlier than disease onset. Thirty-four ultra-high risk (UHR) subjects, 31 first-episode, drug-naive patients with schizophrenia and 37 healthy controls were enrolled for a resting-state scan. The imaging data were analyzed using the seed-based functional connectivity (FC) method. Compared with the controls, UHR subjects and patients with schizophrenia shared increased connectivity between the right Crus I and bilateral posterior cingulate cortex/precuneus and between Lobule IX and the left superior medial prefrontal cortex. There are positive correlations between the right Crus I-bilateral precuneus connectivity and clinical variables (Structured Interview for Prodromal Syndromes/Positive and Negative Symptom Scale negative symptoms/total scores) in the UHR subjects. Increased cerebellar-DMN connectivity shared by the UHR subjects and the patients not only highlights the importance of the DMN in the pathophysiology of psychosis but also may be a trait alteration for psychosis.
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Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.
Wang, Guan; Sun, Yu; Wang, Jianxin
2017-01-01
Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.
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Cyclodextrin-based nanosponges as drug carriers
Directory of Open Access Journals (Sweden)
Francesco Trotta
2012-11-01
Full Text Available Cyclodextrin-based nanosponges, which are proposed as a new nanosized delivery system, are innovative cross-linked cyclodextrin polymers nanostructured within a three-dimensional network. This type of cyclodextrin polymer can form porous insoluble nanoparticles with a crystalline or amorphous structure and spherical shape or swelling properties. The polarity and dimension of the polymer mesh can be easily tuned by varying the type of cross-linker and degree of cross-linking. Nanosponge functionalisation for site-specific targeting can be achieved by conjugating various ligands on their surface. They are a safe and biodegradable material with negligible toxicity on cell cultures and are well-tolerated after injection in mice. Cyclodextrin-based nanosponges can form complexes with different types of lipophilic or hydrophilic molecules. The release of the entrapped molecules can be varied by modifying the structure to achieve prolonged release kinetics or a faster release. The nanosponges could be used to improve the aqueous solubility of poorly water-soluble molecules, protect degradable substances, obtain sustained delivery systems or design innovative drug carriers for nanomedicine.
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The role of networks and artificial intelligence in nanotechnology design and analysis.
Hudson, D L; Cohen, M E
2004-05-01
Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.
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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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Benson, Neil
2015-08-01
Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.