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      The drug cocktail network

      research-article
      1 , 2 , 3 , 4 , 2 ,
      BMC Systems Biology
      BioMed Central
      The 5th IEEE International Conference on Computational Systems Biology (ISB 2011)
      02-04 September 2011

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          Abstract

          Background

          Combination of different agents is widely used in clinic to combat complex diseases with improved therapy and reduced side effects. However, the identification of effective drug combinations remains a challenging task due to the huge number of possible combinations among candidate drugs that makes it impractical to screen putative combinations.

          Results

          In this work, we construct a 'drug cocktail network' using all the known effective drug combinations extracted from the Drug Combination Database (DCDB), and propose a network-based approach to investigate drug combinations. Our results show that the agents in an effective combination tend to have more similar therapeutic effects and share more interaction partners. Based on our observations, we further develop a statistical approach termed as DCPred ( Drug Combination Predictor) to predict possible drug combinations by exploiting the topological features of the drug cocktail network. Validating on the known drug combinations, DCPred achieves the overall AUC (Area Under the receiver operating characteristic Curve) score of 0.92, indicating the predictive power of our proposed approach.

          Conclusions

          The drug cocktail network constructed in this work provides useful insights into the underlying rules of effective drug combinations and offer important clues to accelerate the future discovery of new drug combinations.

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          Most cited references38

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          The structure and function of complex networks

          M. Newman (2003)
          Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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            Specificity and stability in topology of protein networks

            Molecular networks guide the biochemistry of a living cell on multiple levels: its metabolic and signalling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks we quantify correlations between connectivities of interacting nodes and compare them to a null model of a network, in which al links were randomly rewired. We find that for both interaction and regulatory networks, links between highly connected proteins are systematically suppressed, while those between a highly-connected and low-connected pairs of proteins are favored. This effect decreases the likelihood of cross talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations.
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              Preclinical overview of sorafenib, a multikinase inhibitor that targets both Raf and VEGF and PDGF receptor tyrosine kinase signaling.

              Although patients with advanced refractory solid tumors have poor prognosis, the clinical development of targeted protein kinase inhibitors offers hope for the future treatment of many cancers. In vivo and in vitro studies have shown that the oral multikinase inhibitor, sorafenib, inhibits tumor growth and disrupts tumor microvasculature through antiproliferative, antiangiogenic, and/or proapoptotic effects. Sorafenib has shown antitumor activity in phase II/III trials involving patients with advanced renal cell carcinoma and hepatocellular carcinoma. The multiple molecular targets of sorafenib (the serine/threonine kinase Raf and receptor tyrosine kinases) may explain its broad preclinical and clinical activity. This review highlights the antitumor activity of sorafenib across a variety of tumor types, including renal cell, hepatocellular, breast, and colorectal carcinomas in the preclinical setting. In particular, preclinical evidence that supports the different mechanisms of action of sorafenib is discussed.
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                Author and article information

                Conference
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central
                1752-0509
                2012
                16 July 2012
                : 6
                : Suppl 1
                : S5
                Affiliations
                [1 ]Department of Mathematics, Shanghai University, Shanghai 200444, China
                [2 ]Institute of Systems Biology, Shanghai University, Shanghai 200444, China
                [3 ]National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
                [4 ]Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC 3800, Australia
                Article
                1752-0509-6-S1-S5
                10.1186/1752-0509-6-S1-S5
                3403482
                23046711
                810bdfb4-8ebf-4200-ac11-636b43a3afbe
                Copyright ©2012 Xu et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                The 5th IEEE International Conference on Computational Systems Biology (ISB 2011)
                Zhuhai, China
                02-04 September 2011
                History
                Categories
                Research

                Quantitative & Systems biology
                Quantitative & Systems biology

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