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      Mapping the perturbome network of cellular perturbations

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          Abstract

          Drug combinations provide effective treatments for diverse diseases, but also represent a major cause of adverse reactions. Currently there is no systematic understanding of how the complex cellular perturbations induced by different drugs influence each other. Here, we introduce a mathematical framework for classifying any interaction between perturbations with high-dimensional effects into 12 interaction types. We apply our framework to a large-scale imaging screen of cell morphology changes induced by diverse drugs and their combination, resulting in a perturbome network of 242 drugs and 1832 interactions. Our analysis of the chemical and biological features of the drugs reveals distinct molecular fingerprints for each interaction type. We find a direct link between drug similarities on the cell morphology level and the distance of their respective protein targets within the cellular interactome of molecular interactions. The interactome distance is also predictive for different types of drug interactions.

          Abstract

          Our understanding of the mechanisms of drug interactions remains limited. Here the authors introduce a framework to study how complex cellular perturbations induced by different drugs affect each other in morphological feature space.

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          Most cited references37

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          Membrane transporters in drug development.

          Membrane transporters can be major determinants of the pharmacokinetic, safety and efficacy profiles of drugs. This presents several key questions for drug development, including which transporters are clinically important in drug absorption and disposition, and which in vitro methods are suitable for studying drug interactions with these transporters. In addition, what criteria should trigger follow-up clinical studies, and which clinical studies should be conducted if needed. In this article, we provide the recommendations of the International Transporter Consortium on these issues, and present decision trees that are intended to help guide clinical studies on the currently recognized most important drug transporter interactions. The recommendations are generally intended to support clinical development and filing of a new drug application. Overall, it is advised that the timing of transporter investigations should be driven by efficacy, safety and clinical trial enrolment questions (for example, exclusion and inclusion criteria), as well as a need for further understanding of the absorption, distribution, metabolism and excretion properties of the drug molecule, and information required for drug labelling.
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            Mechanisms of drug combinations: interaction and network perspectives.

            Understanding the molecular mechanisms underlying synergistic, potentiative and antagonistic effects of drug combinations could facilitate the discovery of novel efficacious combinations and multi-targeted agents. In this article, we describe an extensive investigation of the published literature on drug combinations for which the combination effect has been evaluated by rigorous analysis methods and for which relevant molecular interaction profiles of the drugs involved are available. Analysis of the 117 drug combinations identified reveals general and specific modes of action, and highlights the potential value of molecular interaction profiles in the discovery of novel multicomponent therapies.
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              The Comparative Toxicogenomics Database: update 2017

              The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.
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                Author and article information

                Contributors
                jmenche@cemm.oeaw.ac.at
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 November 2019
                13 November 2019
                2019
                : 10
                : 5140
                Affiliations
                [1 ]ISNI 0000 0004 0392 6802, GRID grid.418729.1, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, ; Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
                [2 ]ISNI 0000 0001 1271 4623, GRID grid.18886.3f, Present Address: Cancer Research UK Cancer Therapeutics Unit, , The Institute of Cancer Research, ; London, UK
                [3 ]ISNI 0000 0004 5929 4381, GRID grid.417815.e, Present Address: Hit Discovery, Discovery Sciences, R&D, , AstraZeneca, ; Alderley Park, Macclesfield, UK
                Author information
                http://orcid.org/0000-0003-3218-6324
                http://orcid.org/0000-0002-1583-6404
                Article
                13058
                10.1038/s41467-019-13058-9
                6853941
                31723137
                686ad947-1a58-49bd-8ed1-0e015ba7f3c9
                © 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 2019
                : 15 October 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001821, Vienna Science and Technology Fund (Wiener Wissenschafts-, Forschungs- und Technologiefonds);
                Award ID: VRG15-005
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                networks and systems biology,network topology,modularity,regulatory networks
                Uncategorized
                networks and systems biology, network topology, modularity, regulatory networks

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