14,497 research outputs found

    Personalized medicine : the impact on chemistry

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    An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry’s relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies

    Protein-protein interactions: network analysis and applications in drug discovery

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    Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets

    Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

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    Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

    Towards precision medicine for hypertension: a review of genomic, epigenomic, and microbiomic effects on blood pressure in experimental rat models and humans

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    Compelling evidence for the inherited nature of essential hypertension has led to extensive research in rats and humans. Rats have served as the primary model for research on the genetics of hypertension resulting in identification of genomic regions that are causally associated with hypertension. In more recent times, genome-wide studies in humans have also begun to improve our understanding of the inheritance of polygenic forms of hypertension. Based on the chronological progression of research into the genetics of hypertension as the "structural backbone," this review catalogs and discusses the rat and human genetic elements mapped and implicated in blood pressure regulation. Furthermore, the knowledge gained from these genetic studies that provide evidence to suggest that much of the genetic influence on hypertension residing within noncoding elements of our DNA and operating through pervasive epistasis or gene-gene interactions is highlighted. Lastly, perspectives on current thinking that the more complex "triad" of the genome, epigenome, and the microbiome operating to influence the inheritance of hypertension, is documented. Overall, the collective knowledge gained from rats and humans is disappointing in the sense that major hypertension-causing genes as targets for clinical management of essential hypertension may not be a clinical reality. On the other hand, the realization that the polygenic nature of hypertension prevents any single locus from being a relevant clinical target for all humans directs future studies on the genetics of hypertension towards an individualized genomic approach
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