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      Systematic identification of proteins that elicit drug side effects

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          • For more than half of the investigated side effects, we can predict causal proteins.

          • Off-targets contribute slightly more to the explained side effects than main targets.

          • With the current data, we are most successful in explaining the side effects of drugs that target G protein-coupled receptors.

          • Activation of HTR7 causes hyperesthesia in mice, explaining a side effect of triptan drugs.

          Abstract

          Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.

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          Most cited references26

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          The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics

          The Chemistry Development Kit (CDK) is a freely available open-source Java library for Structural Chemo-and Bioinformatics. Its architecture and capabilities as well as the development as an open-source project by a team of international collaborators from academic and industrial institutions is described. The CDK provides methods for many common tasks in molecular informatics, including 2D and 3D rendering of chemical structures, I/O routines, SMILES parsing and generation, ring searches, isomorphism checking, structure diagram generation, etc. Application scenarios as well as access information for interested users and potential contributors are given.
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            Molecular, pharmacological and functional diversity of 5-HT receptors.

            Serotonin (5-hydroxytryptamine, 5-HT) is probably unique among the monoamines in that its effects are subserved by as many as 13 distinct heptahelical, G-protein-coupled receptors (GPCRs) and one (presumably a family of) ligand-gated ion channel(s). These receptors are divided into seven distinct classes (5-HT(1) to 5-HT(7)) largely on the basis of their structural and operational characteristics. Whilst this degree of physical diversity clearly underscores the physiological importance of serotonin, evidence for an even greater degree of operational diversity continues to emerge. The challenge for modern 5-HT research has therefore been to define more precisely the properties of the systems that make this incredible diversity possible. Much progress in this regard has been made during the last decade with the realisation that serotonin is possibly the least conservative monoamine transmitter and the cloning of its many receptors. Coupled with the actions of an extremely avid and efficient reuptake system, this array of receptor subtypes provides almost limitless signalling capabilities to the extent that one might even question the need for other transmitter systems. However, the complexity of the system appears endless, since posttranslational modifications, such as alternate splicing and RNA editing, increase the number of proteins, oligomerisation and heteromerisation increase the number of complexes, and multiple G-protein suggest receptor trafficking, allowing phenotypic switching and crosstalk within and possibly between receptor families. Whether all these possibilities are used in vivo under physiological or pathological conditions remains to be firmly established, but in essence, such variety will keep the 5-HT community busy for quite some time. Those who may have predicted that molecular biology would largely simplify the life of pharmacologists have missed the point for 5-HT research in particular and, most probably, for many other transmitters. This chapter is an attempt to summarise very briefly 5-HT receptor diversity. The reward for unravelling this complex array of serotonin receptor--effector systems may be substantial, the ultimate prize being the development of important new drugs in a range of disease areas.
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              Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets

              Summary Discovering the unintended “off-targets” that predict adverse drug reactions (ADRs) is daunting by empirical methods alone. Drugs can act on multiple protein targets, some of which can be unrelated by traditional molecular metrics, and hundreds of proteins have been implicated in side effects. We therefore explored a computational strategy to predict the activity of 656 marketed drugs on 73 unintended “side effect” targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a Drug-Target-ADR network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic estrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme COX-1. The clinical relevance of this inhibition was borne-out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.
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                Author and article information

                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                Molecular Systems Biology
                Nature Publishing Group
                1744-4292
                2013
                30 April 2013
                30 April 2013
                : 9
                : 663
                Affiliations
                [1 ]Structural and Computational Biology Unit, European Molecular Biology Laboratory , Heidelberg, Germany
                [2 ]Mouse Biology Unit, European Molecular Biology Laboratory , Monterotondo, Italy
                [3 ]Max-Delbrück-Centre for Molecular Medicine , Berlin, Germany
                Author notes
                [a ]Structural and Computational Biology Unit, European Molecular Biology Laboratory , Meyerhofstrasse 1, Heidelberg 69117, Germany. Tel.:+49 6221 387 8526; Fax:+49 6221 387 517; bork@ 123456embl.de
                [*]

                Present address: Biotechnology Center, TU Dresden, 01062 Dresden, Germany

                [†]

                Present address: Preclinical Phenotyping Facility, Campus Science Support Facilities GmbH, Dr Bohr Gasse 3, 1030 Vienna, Austria

                [‡]

                Present address: Institute for Bioinformatics and Systems Biology (MIPS), Helmholtz Center Munich—German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

                [§]

                Present address: Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark

                Article
                msb201310
                10.1038/msb.2013.10
                3693830
                23632385
                5dbbfdae-6022-4e09-b5ca-fba3009964b3
                Copyright © 2013, EMBO and Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit

                History
                : 20 November 2012
                : 17 February 2013
                Categories
                Article

                Quantitative & Systems biology
                computational biology,drug targets,side effects
                Quantitative & Systems biology
                computational biology, drug targets, side effects

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