6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/06/24

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

7 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/06/24

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Molecular Docking for Identification of Potential Targets for Drug Repurposing.

Curr Top Med Chem. 2016 May 30;

Authors: Luo H, Mattes W, Mendrick DL, Hong H

Abstract
Using existing drugs for new indications (drug repurposing) is an effective method not only to reduce drug development time and costs but also to develop treatments for new disease including those that are rare. In order to discover novel indications, potential target identification is a necessary step. One widely used method to identify potential targets is through molecule docking. It requires no prior information except structure inputs from both the drug and the target, and can identify potential targets for a given drug, or identify potential drugs for a specific target. Though molecular docking is popular for drug development and repurposing, challenges remain for the method. In order to improve the prediction accuracy, optimizing the target conformation, considering the solvents and adding co-binders to the system are possible solutions.

PMID: 27334201 [PubMed - as supplied by publisher]

Drug repositioning through network pharmacology.

Curr Top Med Chem. 2016 May 30;

Authors: Ye H, Wei J, Tang K, Feuers R, Hong H

Abstract
Low drug productivity has been a significant problem of the pharmaceutical industry for several decades even though numerous novel technologies were introduced during this period. Currently pharmacologic dogma, "single drug, single target, single disease", is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement established and guiding pharmacologic approaches. The rationale for network pharmacology as a major component of drug discovery and development is that a disease can be caused by perturbation of the disease-causing network and a drug may be designed to interact with multiple targets for modulation of such a network from the disease status toward normal status. Therefore, network pharmacology has been applied to guide and assist in drug repositioning. Drugs exerting their therapeutic effects may directly target disease-associated proteins, but they may also modulate the pathways involved in the pathological process. In this review, we discuss the progresses and prospects in network pharmacology, focusing on drug off-targets discovery, disease-associated protein identification, and pathway analysis for elucidating relationships between drug targets and disease-associated proteins.

PMID: 27334200 [PubMed - as supplied by publisher]

Methods to Profile the Macromolecular Targets of Small Compounds.

Curr Top Med Chem. 2016 May 30;

Authors: Zhu J, Wang W, Chen X

Abstract
Small compounds constitute most of the available medicines. However, their stereophysical and stereochemical properties are relatively simple, which typically results in promiscuity in their interactions with human proteins. Such promiscuity has caused problems in our past efforts to discover and develop new drugs, but at the same time, it also brought new opportunities to exploit the off-target interactions between small compounds and human proteins for novel or improved therapeutics, e.g. in the applications of polypharmacology, drug repositioning, and designing drug combinations. In this direction, identifying the full profile of macromolecules that a small compound may interact with is of fundamental importance to harnessing the positive side of small compound promiscuity. This review summarizes available experimental and computational approaches that identify macromolecular targets for small compounds. The principle, application, performance, limitation and availability of these approaches are discussed.

PMID: 27334197 [PubMed - as supplied by publisher]

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources.

PLoS Comput Biol. 2016 Jun;12(6):e1004989

Authors: Waagmeester A, Kutmon M, Riutta A, Miller R, Willighagen EL, Evelo CT, Pico AR

Abstract
The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

PMID: 27336457 [PubMed - as supplied by publisher]

TarNet: An Evidence-Based Database for Natural Medicine Research.

PLoS One. 2016;11(6):e0157222

Authors: Hu R, Ren G, Sun G, Sun X

Abstract
BACKGROUND: Complex diseases seriously threaten human health. Drug discovery approaches based on "single genes, single drugs, and single targets" are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.
RESULTS: In this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein-protein interactions (PPIs). TarNet includes in-depth information on both plant-compound-protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein-protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound-protein relationships.
CONCLUSIONS: TarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.

PMID: 27337171 [PubMed - as supplied by publisher]

Principles of Systems Biology-No. 6.

Cell Syst. 2016 Jun 22;2(6):356-359

Authors:

Abstract
This month's Cell Systems Call (Cell Systems 1, 307) shows how interdisciplinary approaches can provide leverage against problems as diverse as tracing cell lineage and understanding massive cellular machines.

PMID: 27336964 [PubMed - as supplied by publisher]

Schizophrenia interactome with 504 novel protein-protein interactions.

NPJ Schizophr. 2016;2:16012

Authors: Ganapathiraju MK, Thahir M, Handen A, Sarkar SN, Sweet RA, Nimgaonkar VL, Loscher CE, Bauer EM, Chaparala S

Abstract
Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein-protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities.

PMID: 27336055 [PubMed]

Proteomic systems evaluation of the molecular validity of preclinical psychosis models compared to schizophrenia brain pathology.

Schizophr Res. 2016 Jun 19;

Authors: Cox DA, Gottschalk MG, Wesseling H, Ernst A, Cooper JD, Bahn S

Abstract
Pharmacological and genetic rodent models of schizophrenia play an important role in the drug discovery pipeline, but quantifying the molecular similarity of such models with the underlying human pathophysiology has proved difficult. We developed a novel systems biology methodology for the direct comparison of anterior prefrontal cortex tissue from four established glutamatergic rodent models and schizophrenia patients, enabling the evaluation of which model displays the greatest similarity to schizophrenia across different pathophysiological characteristics of the disease. Liquid chromatography coupled tandem mass spectrometry (LC-MS(E)) proteomic profiling was applied comparing healthy and "disease state" in human post-mortem samples and rodent brain tissue samples derived from models based on acute and chronic phencyclidine (PCP) treatment, ketamine treatment or NMDA receptor knockdown. Protein-protein interaction networks were constructed from significant abundance changes and enrichment analyses enabled the identification of five functional domains of the disease such as "development and differentiation", which were represented across all four rodent models and were thus subsequently used for cross-species comparison. Kernel-based machine learning techniques quantified that the chronic PCP model represented schizophrenia brain changes most closely for four of these functional domains. This is the first study aiming to quantify which rodent model recapitulates the neuropathological features of schizophrenia most closely, providing an indication of face validity as well as potential guidance in the refinement of construct and predictive validity. The methodology and findings presented here support recent efforts to overcome translational hurdles of preclinical psychiatric research by associating functional dimensions of behaviour with distinct biological processes.

PMID: 27335180 [PubMed - as supplied by publisher]

Drug repositioning through network pharmacology.

Curr Top Med Chem. 2016 May 30;

Authors: Ye H, Wei J, Tang K, Feuers R, Hong H

Abstract
Low drug productivity has been a significant problem of the pharmaceutical industry for several decades even though numerous novel technologies were introduced during this period. Currently pharmacologic dogma, "single drug, single target, single disease", is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement established and guiding pharmacologic approaches. The rationale for network pharmacology as a major component of drug discovery and development is that a disease can be caused by perturbation of the disease-causing network and a drug may be designed to interact with multiple targets for modulation of such a network from the disease status toward normal status. Therefore, network pharmacology has been applied to guide and assist in drug repositioning. Drugs exerting their therapeutic effects may directly target disease-associated proteins, but they may also modulate the pathways involved in the pathological process. In this review, we discuss the progresses and prospects in network pharmacology, focusing on drug off-targets discovery, disease-associated protein identification, and pathway analysis for elucidating relationships between drug targets and disease-associated proteins.

PMID: 27334200 [PubMed - as supplied by publisher]

JAK inhibition in the treatment of diabetic kidney disease.

Diabetologia. 2016 Jun 22;

Authors: Brosius FC, Tuttle KR, Kretzler M

Abstract
Diabetic kidney disease (DKD) is the most common cause of kidney failure in many countries today, but treatments have not improved in the last 20 years. Recently, systems biology methods have allowed the elucidation of signalling pathways and networks involved in the progression of DKD that were not well appreciated previously. A prominent pathway found to be integrally associated with DKD progression is the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway. Increased expression of JAK-STAT genes was found in multiple cells in the kidney, including glomerular podocytes, in both early and progressive DKD. Subsequent experiments in a mouse diabetic model showed that enhanced expression of JAK2 selectively in glomerular podocytes increased functional and pathological features of DKD. Finally, a yet unpublished Phase 2 multicentre, randomised, double-blind, placebo-controlled study of the efficacy of a selective JAK1 and JAK2 inhibitor has been conducted in type 2 diabetic participants with DKD. In this trial there was a reduction of albuminuria in participants who received the active inhibitor compared with those who received a placebo These results support the further study of JAK inhibitors as a new therapy for DKD. This review summarises a presentation given at the 'Anti-inflammatory interventions in diabetes' symposium at the 2015 annual meeting of the EASD. It is accompanied by an overview by the Session Chair, Hiddo Heerspink (DOI: 10.1007/s00125-016-4030-4 ).

PMID: 27333885 [PubMed - as supplied by publisher]

Supporting systematic reviews using LDA-based document representations.

Syst Rev. 2015;4:172

Authors: Mo Y, Kontonatsios G, Ananiadou S

Abstract
BACKGROUND: Identifying relevant studies for inclusion in a systematic review (i.e. screening) is a complex, laborious and expensive task. Recently, a number of studies has shown that the use of machine learning and text mining methods to automatically identify relevant studies has the potential to drastically decrease the workload involved in the screening phase. The vast majority of these machine learning methods exploit the same underlying principle, i.e. a study is modelled as a bag-of-words (BOW).
METHODS: We explore the use of topic modelling methods to derive a more informative representation of studies. We apply Latent Dirichlet allocation (LDA), an unsupervised topic modelling approach, to automatically identify topics in a collection of studies. We then represent each study as a distribution of LDA topics. Additionally, we enrich topics derived using LDA with multi-word terms identified by using an automatic term recognition (ATR) tool. For evaluation purposes, we carry out automatic identification of relevant studies using support vector machine (SVM)-based classifiers that employ both our novel topic-based representation and the BOW representation.
RESULTS: Our results show that the SVM classifier is able to identify a greater number of relevant studies when using the LDA representation than the BOW representation. These observations hold for two systematic reviews of the clinical domain and three reviews of the social science domain.
CONCLUSIONS: A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening. The proposed term-enriched topics are more informative and less ambiguous to systematic reviewers.

PMID: 26612232 [PubMed - indexed for MEDLINE]

8 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/06/23

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Human Pluripotent Stem Cell-derived Cortical Neurons for High Throughput Medication Screening in Autism: A Proof of Concept Study in SHANK3 Haploinsufficiency Syndrome.

EBioMedicine. 2016 May 27;

Authors: Darville H, Poulet A, Rodet-Amsellem F, Chatrousse L, Pernelle J, Boissart C, Héron D, Nava C, Perrier A, Jarrige M, Cogé F, Millan MJ, Bourgeron T, Peschanski M, Delorme R, Benchoua A

Abstract
Autism spectrum disorders affect millions of individuals worldwide, but their heterogeneity complicates therapeutic intervention that is essentially symptomatic. A versatile yet relevant model to rationally screen among hundreds of therapeutic options would help improving clinical practice. Here we investigated whether neurons differentiated from pluripotent stem cells can provide such a tool using SHANK3 haploinsufficiency as a proof of principle. A library of compounds was screened for potential to increase SHANK3 mRNA content in neurons differentiated from control human embryonic stem cells. Using induced pluripotent stem cell technology, active compounds were then evaluated for efficacy in correcting dysfunctional networks of neurons differentiated from individuals with deleterious point mutations of SHANK3. Among 202 compounds tested, lithium and valproic acid showed the best efficacy at corrected SHANK3 haploinsufficiency associated phenotypes in cellulo. Lithium pharmacotherapy was subsequently provided to one patient and, after one year, an encouraging decrease in autism severity was observed. This demonstrated that pluripotent stem cell-derived neurons provide a novel cellular paradigm exploitable in the search for specific disease-modifying treatments.

PMID: 27333044 [PubMed - as supplied by publisher]

Understanding New Types of Evidence Ready for Translation into Nursing Informatics.

Stud Health Technol Inform. 2016;225:686-688

Authors: McCormick K

Abstract
Nurses are the primary deliverers of patient care and observers of patient side effects to medications. The primary objective of this tutorial is to bring the participants up to date in genomic applications for nursing from birth until death. A secondary objective is to define at least 17 pharmacogenomics evidence guidelines ready for implementation into the Electronic Health Record. The target audience are nurses in practice, implementers of EHRs, nursing in leadership and policy-making positions, those focused on defining new areas for nursing research, and educators who are in need of defining criteria for integrating genomics into nursing education.

PMID: 27332305 [PubMed - as supplied by publisher]

Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs.

Oncotarget. 2016 Jun 14;

Authors: Li J, Lei K, Wu Z, Li W, Liu G, Liu J, Cheng F, Tang Y

Abstract
As the recent development of high-throughput technologies in cancer pharmacogenomics, there is an urgent need to develop new computational approaches for comprehensive identification of new pharmacogenomic biomarkers, such as microRNAs (miRNAs). In this study, a network-based framework, namely the SMiR-NBI model, was developed to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs on the basis of a heterogeneous network connecting drugs, miRNAs and genes. A high area under the receiver operating characteristic curve of 0.820 ± 0.013 was yielded during 10-fold cross validation. In addition, high performance was further validated in identifying new anticancer mechanism-of-action for natural products and non-steroidal anti-inflammatory drugs. Finally, the newly predicted miRNAs for tamoxifen and metformin were experimentally validated in MCF-7 and MDA-MB-231 breast cancer cell lines via qRT-PCR assays. High success rates of 60% and 65% were yielded for tamoxifen and metformin, respectively. Specifically, 11 oncomiRNAs (e.g. miR-20a-5p, miR-27a-3p, miR-29a-3p, and miR-146a-5p) from the top 20 predicted miRNAs were experimentally verified as new pharmacogenomic biomarkers for metformin in MCF-7 or MDA-MB-231 cell lines. In summary, the SMiR-NBI model would provide a powerful tool to identify potential pharmacogenomic biomarkers characterized by miRNAs in the emerging field of precision cancer medicine, which is available at http://lmmd.ecust.edu.cn/database/smir-nbi/.

PMID: 27329603 [PubMed - as supplied by publisher]

Considerations, challenges and future of anti-TNF therapy in treating inflammatory bowel disease.

Expert Opin Biol Ther. 2016 Jun 22;

Authors: Pouillon L, Bossuyt P, Peyrin-Biroulet L

Abstract
INTRODUCTION: Crohn's disease (CD) and ulcerative colitis (UC) are chronic disabling conditions. Monoclonal antibody therapy directed against tumor necrosis factor-alpha (anti-TNF) has revolutionized the care of patients with inflammatory bowel disease (IBD).
AREAS COVERED: Considerations before starting anti-TNF therapy are highlighted: the best time to start with anti-TNF therapy, either alone or in combination with an immunomodulator, the choice of an anti-TNF agent and the contra-indications to anti-TNF therapy. Primary non-response and secondary loss of response are discussed. The questions of de-escalating therapy, the role of therapeutic drug monitoring and the use of biosimilars, are all marked. Finally, the future directions of anti-TNF therapy are emphasized.
EXPERT OPINION: Anti-TNF therapy remains the cornerstone in the treatment of IBD. When initiating long-term therapy, safety and cost issues are of great importance. The therapeutic armamentarium in the treatment of IBD is rapidly growing. Therefore, the challenge is to optimize the use and refine the exact position of anti-TNF therapy in the near future, with personalized medicine as the ultimate goal.

PMID: 27329436 [PubMed - as supplied by publisher]

Precision medicine: The future in diabetes care?

Diabetes Res Clin Pract. 2016 Jul;117:12-21

Authors: Scheen AJ

Abstract
Personalized medicine aims at better targeting therapeutic intervention to the individual to maximize benefit and minimize harm. Type 2 diabetes (T2D) is a heterogeneous disease from a genetic, pathophysiological and clinical point of view. Thus, the response to any antidiabetic medication may considerably vary between individuals. Numerous glucose-lowering agents, with different mechanisms of action, have been developed, a diversified armamentarium that offers the possibility of a patient-centred therapeutic approach. In the current clinical practice, a personalized approach is only based upon phenotype, taking into account patient and disease individual characteristics. If this approach may help increase both efficacy and safety outcomes, there remains considerable room for improvement. In recent years, many efforts were taken to identify genetic and genotype SNP's (Single Nucleotide Polymorphism's) variants that influence the pharmacokinetics, pharmacodynamics, and ultimately the therapeutic response of oral glucose-lowering drugs. This approach mainly concerns metformin, sulphonylureas, meglitinides and thiazolidinediones, with only scarce data concerning gliptins and gliflozins yet. However, the contribution of pharmacogenetics and pharmacogenomics to personalized therapy still needs to mature greatly before routine clinical implementation is possible. This review discusses both opportunities and challenges of precision medicine and how this new paradigm may lead to a better individualized treatment of T2D.

PMID: 27329017 [PubMed - in process]

Sodium channel Nav1.8: Emerging links to human disease.

Neurology. 2016 Feb 2;86(5):473-83

Authors: Han C, Huang J, Waxman SG

Abstract
The NaV1.8 sodium channel, encoded by gene SCN10A, was initially termed sensory neuron-specific (SNS) due to prominent expression in primary sensory neurons including dorsal root ganglion (DRG) neurons. Early studies on rodent NaV1.8 demonstrated depolarized voltage dependence of channel inactivation, a slow rate of inactivation, and rapid recovery from inactivation. As a result of these biophysical properties, NaV1.8 supports repetitive firing in response to sustained depolarization. This article reviews recent studies that reveal multiple links of NaV1.8 to human disease: (1) It has recently been shown that functional attributes that distinguish NaV1.8 from other sodium channel subtypes are exaggerated in human NaV1.8; its influence on neuronal activity is thus greater than previously thought. (2) Gain-of-function mutations of NaV1.8 that produce DRG neuron hyperexcitability have been found in 3% of patients with painful neuropathy, establishing a role in pathogenesis. (3) NaV1.8 is ectopically expressed within Purkinje neurons in multiple sclerosis (MS), where it perturbs electrical activity. Recent evidence indicates that variants of SCN10A predict the degree of cerebellar dysfunction in MS. (4) Emerging evidence has linked SCN10A variants to disorders of cardiac rhythm, via mechanisms that may include an effect on cardiac innervation. Involvement of NaV1.8 in neurologic disease may have therapeutic implications. NaV1.8-specific blocking agents, under development, ameliorate pain and attenuate MS-like deficits in animal models. Recent studies suggest that pharmacogenomics may permit the matching of specific channel blocking agents to particular patients. The new links of NaV1.8 in human disease raise new questions, but also suggest new therapeutic strategies.

PMID: 26747884 [PubMed - indexed for MEDLINE]