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Showing 4531 to 4534 of 5186 entries
Published on February 25, 2014
A systems biology approach to identify intelligence quotient score-related genomic regions, and pathways relevant to potential therapeutic treatments.
Abstract: Although the intelligence quotient (IQ) is the most popular intelligence test in the world, little is known about the underlying biological mechanisms that lead to the differences in human. To improve our understanding of cognitive processes and identify potential biomarkers, we conducted a comprehensive investigation of 158 IQ-related genes selected from the literature. A genomic distribution analysis demonstrated that IQ-related genes were enriched in seven regions of chromosome 7 and the X chromosome. In addition, these genes were enriched in target lists of seven transcription factors and sixteen microRNAs. Using a network-based approach, we further reconstructed an IQ-related pathway from known human pathway interaction data. Based on this reconstructed pathway, we incorporated enriched drugs and described the importance of dopamine and norepinephrine systems in IQ-related biological process. These findings not only reveal several testable genes and processes related to IQ scores, but also have potential therapeutic implications for IQ-related mental disorders.
Published on February 24, 2014
A pipeline to extract drug-adverse event pairs from multiple data sources.
Abstract: BACKGROUND: Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones. METHOD: We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts. RESULTS: Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further. CONCLUSION: A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.
Published on February 24, 2014
Pathway analysis for drug repositioning based on public database mining.
Abstract: Sixteen FDA-approved drugs were investigated to elucidate their mechanisms of action (MOAs) and clinical functions by pathway analysis based on retrieved drug targets interacting with or affected by the investigated drugs. Protein and gene targets and associated pathways were obtained by data-mining of public databases including the MMDB, PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez E-Utilities were applied, and in-house Ruby scripts were developed for data retrieval and pathway analysis to identify and evaluate relevant pathways common to the retrieved drug targets. Pathways pertinent to clinical uses or MOAs were obtained for most drugs. Interestingly, some drugs identified pathways responsible for other diseases than their current therapeutic uses, and these pathways were verified retrospectively by in vitro tests, in vivo tests, or clinical trials. The pathway enrichment analysis based on drug target information from public databases could provide a novel approach for elucidating drug MOAs and repositioning, therefore benefiting the discovery of new therapeutic treatments for diseases.
Published on February 20, 2014
Genetics of rheumatoid arthritis contributes to biology and drug discovery.
Abstract: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating approximately 10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
Published on February 20, 2014
DanQi Pill protects against heart failure through the arachidonic acid metabolism pathway by attenuating different cyclooxygenases and leukotrienes B4.
Abstract: BACKGROUND: Chinese herbal formulae are composed of complex components and produce comprehensive pharmacological effects. Unlike chemical drugs that have only one clear single target, the components of Chinese herbal formulae have multiple channels and targets. How to discover the pharmacological targets of Chinese herbal formulae and their underlying molecular mechanism are still under investigation. METHODS: DanQi pill (DQP), which is one of the widely prescribed traditional Chinese medicines, is applied as an example drug. In this study, we used the drug target prediction model (DrugCIPHER-CS) to examine the underlying molecular mechanism of DQP, followed by experimental validation. RESULTS: A novel therapeutic effect pattern of DQP was identified. After determining the compounds in DQP, we used DrugCIPHER-CS to predict their potential targets. These potential targets were significantly enriched in well-known cardiovascular disease-related pathways. For example, the biological processes of neuroactive ligand-receptor interaction, calcium-signaling pathway, and aminoacyl-tRNA biosynthesis were involved. A new and significant pathway, arachidonic acid (AA) metabolism, was also identified in this study. This predicted pathway alteration was validated with an animal model of heart failure (HF). Results show that DQP had effect both on thromboxane B2 (TXB2) and Prostaglandin I2 (PGI2) in different patterns. It can down-regulate the TXB2 and up-regulate the PGI2 in diverse way. Remarkably, it also had effect on cyclooxygenase (COX)-1 and COX2 by suppressing their levels, which may be the critical and novel mechanism of cardiacprotective efficacy for DQP. Furthermore, leukotrienes B4 (LTB4) receptor, another key molecule of AA metabolism which finally mediated gastrotoxic leukotrienes, was also reduced by DQP. CONCLUSIONS: The combination of drug target prediction and experimental validation provides new insights into the complicated mechanism of DQP.
Published on February 15, 2014
Large scale integration of drug-target information reveals poly-pharmacological drug action mechanisms in tumor cell line growth inhibition assays.
Abstract: Understanding therapeutic mechanisms of drug anticancer cytotoxicity represents a key challenge in preclinical testing. Here we have performed a meta-analysis of publicly available tumor cell line growth inhibition assays (~ 70 assays from 6 independent experimental groups covering ~ 500 000 molecules) with the primary goal of understanding molecular therapeutic mechanisms of cancer cytotoxicity. To implement this we have collected currently available information on protein targets for molecules that were tested in the assays. We used a statistical methodology to identify protein targets overrepresented among molecules exhibiting cancer cytotoxicity with the particular focus of identifying overrepresented patterns consisting of several proteins (i.e. proteins "A" and "B" and "C"). Our analysis demonstrates that targeting individual proteins can result in a significant increase (up to 50-fold) of the observed odds for a molecule to be an efficient inhibitor of tumour cell line growth. However, further insight into potential molecular mechanisms reveals a multi-target mode of action: targeting a pattern of several proteins drastically increases the observed odds (up to 500-fold) for a molecule to be tumour cytotoxic. In contrast, molecules targeting only one protein but not targeting an additional set of proteins tend to be nontoxic. Our findings support a poly-pharmacology drug discovery paradigm, demonstrating that anticancer cytotoxicity is a product, in most cases, of multi-target mode of drug action.
Published on February 11, 2014
RNAi-mediated silencing of MLL-AF9 reveals leukemia-associated downstream targets and processes.
Abstract: BACKGROUND: The translocation t(9;11)(p22;q23) leading to the leukemogenic fusion gene MLL-AF9 is a frequent translocation in infant acute myeloid leukemia (AML). This study aimed to identify genes and molecular processes downstream of MLL-AF9 (alias MLL-MLLT3) which could assist to develop new targeted therapies for such leukemia with unfavorable prognosis. METHODS: In the AML cell line THP1 which harbors this t(9;11) translocation, endogenous MLL-AF9 was silenced via siRNA while ensuring specificity of the knockdown and its efficiency on functional protein level. RESULTS: The differential gene expression profile was validated for leukemia-association by gene set enrichment analysis of published gene sets from patient studies and MLL-AF9 overexpression studies and revealed 425 differentially expressed genes. Gene ontology analysis was consistent with a more differentiated state of MLL-AF9 depleted cells, with involvement of a wide range of downstream transcriptional regulators and with defined functional processes such as ribosomal biogenesis, chaperone binding, calcium homeostasis and estrogen response. We prioritized 41 gene products as candidate targets including several novel and potentially druggable effectors of MLL-AF9 (AHR, ATP2B2, DRD5, HIPK2, PARP8, ROR2 and TAS1R3). Applying the antagonist SCH39166 against the dopamine receptor DRD5 resulted in reduced leukemic cell characteristics of THP1 cells. CONCLUSION: Besides potential new therapeutic targets, the described transcription profile shaped by MLL-AF9 provides an information source into the molecular processes altered in MLL aberrant leukemia.
Published on February 6, 2014
DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information.
Abstract: The use of existing drugs for new therapeutic applications, commonly referred to as drug repositioning, is a way for fast and cost-efficient drug discovery. Drug repositioning in oncology is commonly initiated by in vitro experimental evidence that a drug exhibits anticancer cytotoxicity. Any independent verification that the observed effects in vitro may be valid in a clinical setting, and that the drug could potentially affect patient survival in vivo is of paramount importance. Despite considerable recent efforts in computational drug repositioning, none of the studies have considered patient survival information in modelling the potential of existing/new drugs in the management of cancer. Therefore, we have developed DRUGSURV; this is the first computational tool to estimate the potential effects of a drug using patient survival information derived from clinical cancer expression data sets. DRUGSURV provides statistical evidence that a drug can affect survival outcome in particular clinical conditions to justify further investigation of the drug anticancer potential and to guide clinical trial design. DRUGSURV covers both approved drugs ( approximately 1700) as well as experimental drugs ( approximately 5000) and is freely available at http://www.bioprofiling.de/drugsurv.
Published on February 5, 2014
Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics.
Abstract: 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.
Published on February 1, 2014
The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation.
Abstract: BACKGROUND: In the European Union, medicines are authorised for some rare disease only if they are judged to be dissimilar to authorised orphan drugs for that disease. This paper describes the use of 2D fingerprints to show the extent of the relationship between computed levels of structural similarity for pairs of molecules and expert judgments of the similarities of those pairs. The resulting relationship can be used to provide input to the assessment of new active compounds for which orphan drug authorisation is being sought. RESULTS: 143 experts provided judgments of the similarity or dissimilarity of 100 pairs of drug-like molecules from the DrugBank 3.0 database. The similarities of these pairs were also computed using BCI, Daylight, ECFC4, ECFP4, MDL and Unity 2D fingerprints. Logistic regression analyses demonstrated a strong relationship between the human and computed similarity assessments, with the resulting regression models having significant predictive power in experiments using data from submissions of orphan drug medicines to the European Medicines Agency. The BCI fingerprints performed best overall on the DrugBank dataset while the BCI, Daylight, ECFP4 and Unity fingerprints performed comparably on the European Medicines Agency dataset. CONCLUSIONS: Measures of structural similarity based on 2D fingerprints can provide a useful source of information for the assessment of orphan drug status by regulatory authorities.
Showing 4531 to 4534 of 5186 entries