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      Network-based prediction of drug combinations

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          Abstract

          Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.

          Abstract

          Combination therapy holds great promise, but discovery remains challenging. Here, the authors propose a method to identify efficacious drug combinations for specific diseases, and find that successful combinations tend to target separate neighbourhoods of the disease module in the human interactome.

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          Most cited references53

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          Identification of common molecular subsequences.

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            Open Babel: An open chemical toolbox

            Background A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. Results We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. Conclusions Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.
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              Network pharmacology: the next paradigm in drug discovery.

              The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.
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                Author and article information

                Contributors
                alb@neu.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 March 2019
                13 March 2019
                2019
                : 10
                : 1197
                Affiliations
                [1 ]ISNI 0000 0001 2173 3359, GRID grid.261112.7, Center for Complex Networks Research and Department of Physics, , Northeastern University, ; Boston, MA 02115 USA
                [2 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Center for Cancer Systems Biology and Department of Cancer Biology, , Dana-Farber Cancer Institute, ; Boston, MA 02215 USA
                [3 ]ISNI 0000 0001 0675 4725, GRID grid.239578.2, Genomic Medicine Institute, Lerner Research Institute, , Cleveland Clinic, ; Cleveland, OH 44106 USA
                [4 ]ISNI 0000 0001 2164 3847, GRID grid.67105.35, Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, , Case Western Reserve University, ; Cleveland, OH 44195 USA
                [5 ]ISNI 0000 0001 2164 3847, GRID grid.67105.35, Case Comprehensive Cancer Center, , Case Western Reserve University School of Medicine, ; Cleveland, OH 44106 USA
                [6 ]ISNI 000000041936754X, GRID grid.38142.3c, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA 02215 USA
                [7 ]ISNI 0000 0001 2149 6445, GRID grid.5146.6, Center for Network Science, , Central European University, ; Budapest, 1051 Hungary
                Article
                9186
                10.1038/s41467-019-09186-x
                6416394
                30867426
                e47f0839-1cf2-47f6-994f-f7ded539f752
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 April 2018
                : 20 February 2019
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