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Showing 4211 to 4214 of 5186 entries
Published on December 9, 2015
Meta- and Orthogonal Integration of Influenza "OMICs" Data Defines a Role for UBR4 in Virus Budding.
Abstract: Several systems-level datasets designed to dissect host-pathogen interactions during influenza A infection have been reported. However, apparent discordance among these data has hampered their full utility toward advancing mechanistic and therapeutic knowledge. To collectively reconcile these datasets, we performed a meta-analysis of data from eight published RNAi screens and integrated these data with three protein interaction datasets, including one generated within the context of this study. Further integration of these data with global virus-host interaction analyses revealed a functionally validated biochemical landscape of the influenza-host interface, which can be queried through a simplified and customizable web portal (http://www.metascape.org/IAV). Follow-up studies revealed that the putative ubiquitin ligase UBR4 associates with the viral M2 protein and promotes apical transport of viral proteins. Taken together, the integrative analysis of influenza OMICs datasets illuminates a viral-host network of high-confidence human proteins that are essential for influenza A virus replication.
Published on December 8, 2015
The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications.
Abstract: Mobile health technologies to detect physiological and simple-analyte biomarkers have been explored for the improvement and cost-reduction of healthcare services, some of which have been endorsed by the US FDA. Advancements in the investigations of non-invasive and minimally-invasive molecular biomarkers and biomarker candidates and the development of portable biomarker detection technologies have fuelled great interests in these new technologies for mhealth applications. But apart from the development of more portable biomarker detection technologies, key questions need to be answered and resolved regarding to the relevance, coverage, and performance of these technologies and the big data management issues arising from their wide spread applications. In this work, we analyzed the newly emerging portable biomarker detection technologies, the 664 non-invasive molecular biomarkers and the 592 potential minimally-invasive blood molecular biomarkers, focusing on their detection capability, affordability, relevance, and coverage. Our analysis suggests that a substantial percentage of these biomarkers together with the new technologies can be potentially used for a variety of disease conditions in mhealth applications. We further propose a new strategy for reducing the workload in the processing and analysis of the big data arising from widespread use of mhealth products, and discuss potential issues of implementing this strategy.
Published on December 8, 2015
The Haemonchus contortus kinome--a resource for fundamental molecular investigations and drug discovery.
Abstract: BACKGROUND: Protein kinases regulate a plethora of essential signalling and other biological pathways in all eukaryotic organisms, but very little is known about them in most parasitic nematodes. METHODS: Here, we defined, for the first time, the entire complement of protein kinases (kinome) encoded in the barber's pole worm (Haemonchus contortus) through an integrated analysis of transcriptomic and genomic datasets using an advanced bioinformatic workflow. RESULTS: We identified, curated and classified 432 kinases representing ten groups, 103 distinct families and 98 subfamilies. A comparison of the kinomes of H. contortus and Caenorhabditis elegans (a related, free-living nematode) revealed considerable variation in the numbers of casein kinases, tyrosine kinases and Ca(2+)/calmodulin-dependent protein kinases, which likely relate to differences in biology, habitat and life cycle between these worms. Moreover, a suite of kinase genes was selectively transcribed in particular developmental stages of H. contortus, indicating central roles in developmental and reproductive processes. In addition, using a ranking system, drug targets (n = 13) and associated small-molecule effectors (n = 1517) were inferred. CONCLUSIONS: The H. contortus kinome will provide a useful resource for fundamental investigations of kinases and signalling pathways in this nematode, and should assist future anthelmintic discovery efforts; this is particularly important, given current drug resistance problems in parasitic nematodes.
Published on December 7, 2015
Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.
Abstract: Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer s disease or Parkinson s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).
Published on December 7, 2015
Identification of Novel Inhibitors of Organic Anion Transporting Polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) Using a Consensus Vote of Six Classification Models.
Abstract: Organic anion transporting polypeptides 1B1 and 1B3 are transporters selectively expressed on the basolateral membrane of the hepatocyte. Several studies reveal that they are involved in drug-drug interactions, cancer, and hyperbilirubinemia. In this study, we developed a set of classification models for OATP1B1 and 1B3 inhibition based on more than 1700 carefully curated compounds from literature, which were validated via cross-validation and by use of an external test set. After combining several sets of descriptors and classifiers, the 6 best models were selected according to their statistical performance and were used for virtual screening of DrugBank. Consensus scoring of the screened compounds resulted in the selection and purchase of nine compounds as potential dual inhibitors and of one compound as potential selective OATP1B3 inhibitor. Biological testing of the compounds confirmed the validity of the models, yielding an accuracy of 90% for OATP1B1 and 80% for OATP1B3, respectively. Moreover, at least half of the new identified inhibitors are associated with hyperbilirubinemia or hepatotoxicity, implying a relationship between OATP inhibition and these severe side effects.
Published on December 4, 2015
Defining the Schistosoma haematobium kinome enables the prediction of essential kinases as anti-schistosome drug targets.
Abstract: The blood fluke Schistosoma haematobium causes urogenital schistosomiasis, a neglected tropical disease (NTD) that affects more than 110 million people. Treating this disease by targeted or mass administration with a single chemical, praziquantel, carries the risk that drug resistance will develop in this pathogen. Therefore, there is an imperative to search for new drug targets in S. haematobium and other schistosomes. In this regard, protein kinases have potential, given their essential roles in biological processes and as targets for drugs already approved by the US Food and Drug Administration (FDA) for use in humans. In this context, we defined here the kinome of S. haematobium using a refined bioinformatic pipeline. We classified, curated and annotated predicted kinases, and assessed the developmental transcription profiles of kinase genes. Then, we prioritised a panel of kinases as potential drug targets and inferred chemicals that bind to them using an integrated bioinformatic pipeline. Most kinases of S. haematobium are very similar to those of its congener, S. mansoni, offering the prospect of designing chemicals that kill both species. Overall, this study provides a global insight into the kinome of S. haematobium and should assist the repurposing or discovery of drugs against schistosomiasis.
Published on December 1, 2015
Polycaprolactone thin-film drug delivery systems: Empirical and predictive models for device design.
Abstract: PURPOSE: To define empirical models and parameters based on theoretical equations to describe drug release profiles from two polycaprolactone thin-film drug delivery systems. Additionally, to develop a predictive model for empirical parameters based on drugs' physicochemical properties. METHODS: Release profiles from a selection of drugs representing the standard pharmaceutical space in both polycaprolactone matrix and reservoir systems were determined experimentally. The proposed models were used to calculate empirical parameters describing drug diffusion and release. Observed correlations between empirical parameters and drug properties were used to develop equations to predict parameters based on drug properties. Predictive and empirical models were evaluated in the design of three prototype devices: a levonorgestrel matrix system for on-demand locally administered contraception, a timolol-maleate reservoir system for glaucoma treatment, and a primaquine-bisphosphate reservoir system for malaria prophylaxis. RESULTS: Proposed empirical equations accurately fit experimental data. Experimentally derived empirical parameters show significant correlations with LogP, molecular weight, and solubility. Empirical models based on predicted parameters accurately predict experimental release data for three prototype systems, demonstrating the accuracy and utility of these models. CONCLUSION: The proposed empirical models can be used to design polycaprolactone thin-film devices for target geometries and release rates. Empirical parameters can be predicted based on drug properties. Together, these models provide tools for preliminary evaluation and design of controlled-release delivery systems.
Published in November 2015
Investigation of Saliva as an Alternative to Plasma Monitoring of Voriconazole.
Abstract: BACKGROUND AND OBJECTIVES: Therapeutic drug monitoring (TDM) of voriconazole is increasingly being implemented in clinical practice. However, as blood sampling can be difficult in paediatric and ambulatory patients, a non-invasive technique for TDM is desirable. The aim of this study was to compare the pharmacokinetics of voriconazole in saliva with the pharmacokinetics of unbound and total voriconazole in plasma in order to clinically validate saliva as an alternative to plasma in voriconazole TDM. METHODS: In this pharmacokinetic study, paired plasma and saliva samples were taken at steady state in adult haematology and pneumology patients treated with voriconazole. Unbound and bound plasma voriconazole concentrations were separated using high-throughput equilibrium dialysis. Voriconazole concentrations were determined with liquid chromatography-tandem mass spectrometry. Pharmacokinetic parameters were calculated using log-linear regression. RESULTS: Sixty-three paired samples were obtained from ten patients (seven haematology and three pneumology patients). Pearson's correlation coefficients (R values) for saliva versus unbound and total plasma voriconazole concentrations showed a very strong correlation, with values of 0.970 (p < 0.001) and 0.891 (p < 0.001), respectively. Linear mixed modelling revealed strong agreement between voriconazole concentrations in saliva and unbound plasma voriconazole concentrations, with a mean bias of -0.03 (95 % confidence interval -0.14 to 0.09; p = 0.60). For total concentrations below 10 mg/L, the mean ratio of saliva to total plasma voriconazole concentrations was 0.51 +/- 0.08 (n = 63), which did not differ significantly (p = 0.76) from the unbound fraction of voriconazole in plasma of 0.49 +/- 0.03 (n = 36). CONCLUSIONS: Saliva can serve as a reliable alternative to plasma in voriconazole TDM, and it can easily be implemented in clinical practice.
Published in November 2015
Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.
Abstract: PURPOSE: The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. METHODS: We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. RESULTS: The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. CONCLUSION: Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.
Published in November 2015
An integrated network platform for contextual prioritization of drugs and pathways.
Abstract: Repurposing of drugs to novel disease indications has promise for faster clinical translation. However, identifying the best drugs for a given pathological context is not trivial. We developed an integrated random walk-based network framework that combines functional biomolecular relationships and known drug-target interactions as a platform for contextual prioritization of drugs, genes and pathways. We show that the use of gene-centric or drug-centric data, such as gene expression data or a phenotypic drug screen, respectively, within this network platform can effectively prioritize drugs and pathways, respectively, to the studied biological context. We demonstrate that various genomic data can be used as contextual cues to effectively prioritize drugs to the studied context, while similarly, phenotypic drug screen data can be used to effectively prioritize genes and pathways to the studied phenotypic context. As a proof-of-principle, we showcase the use of our platform to identify known and novel drug indications against different subsets of breast cancers through contextual prioritization based on genome-wide gene expression, shRNA and drug screen and clinical survival data. The integrated network and associated methods are incorporated into the NetWalker suite for functional genomics analysis ().
Showing 4211 to 4214 of 5186 entries