Blog
About

69
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Automated design of ligands to polypharmacological profiles

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          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

          The clinical efficacy and safety of a drug is determined by its activity profile across multiple proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to rationally design drugs a priori against profiles of multiple proteins would have immense value in drug discovery. We describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads where multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

          Related collections

          Most cited references 61

          • Record: found
          • Abstract: found
          • Article: not found

          The properties of known drugs. 1. Molecular frameworks.

          In order to better understand the common features present in drug molecules, we use shape description methods to analyze a database of commercially available drugs and prepare a list of common drug shapes. A useful way of organizing this structural data is to group the atoms of each drug molecule into ring, linker, framework, and side chain atoms. On the basis of the two-dimensional molecular structures (without regard to atom type, hybridization, and bond order), there are 1179 different frameworks among the 5120 compounds analyzed. However, the shapes of half of the drugs in the database are described by the 32 most frequently occurring frameworks. This suggests that the diversity of shapes in the set of known drugs is extremely low. In our second method of analysis, in which atom type, hybridization, and bond order are considered, more diversity is seen; there are 2506 different frameworks among the 5120 compounds in the database, and the most frequently occurring 42 frameworks account for only one-fourth of the drugs. We discuss the possible interpretations of these findings and the way they may be used to guide future drug discovery research.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Global mapping of pharmacological space.

            We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                12 December 2012
                13 December 2012
                13 June 2013
                : 492
                : 7428
                : 215-220
                Affiliations
                [1 ]Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
                [2 ]NIMH Psychoactive Drug Screening Program, Department of Pharmacology, The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina 27759, USA
                [3 ]Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical School, Durham NC 27710, USA
                [4 ]Department of Pharmacology and Division of Medicinal Chemistry and Natural Products, The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina 27759, USA
                [5 ]Laboratory of Biochemical Neuroendocrinology, Clinical Research Institute of Montreal (IRCM), affiliated with the University of Montreal, Montreal, Quebec, H2W 1R7, Canada
                [6 ]Ecole Polytechnique Fédérale de Lausanne (EPFL) SV ISREC, Station 19, CH-1015 Lausanne, Switzerland
                [7 ]Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurobiology, Duke University Medical School, Durham, NC 27710, USA
                Author notes
                Correspondence or request for materials should be addressed to A.L.H. ( a.hopkins@ 123456dundee.ac.uk ) or B.L.R ( bryan_roth@ 123456med.unc.edu ).
                Article
                EMS50247
                10.1038/nature11691
                3653568
                23235874

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: Wellcome Trust :
                Award ID: 083481 || WT
                Categories
                Article

                Uncategorized

                Comments

                Comment on this article