DOCSLIB.ORG

Stardrop Refernce Guide

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Stardrop Refernce Guide © 2015 Optibrium Ltd. Optibrium™, StarDrop™, Glowing Molecule™, Nova™, Auto-Modeller™, Card View™ and MPO Explorer™ are trademarks of Optibrium Ltd. BIOSTER™ is a trademark of Digital Chemistry Ltd., Derek Nexus™ is a trademark of Lhasa Ltd., torch3D™ is a trademark of Cresset Biomolecular Research Ltd. and Matsy™ is a trademark of NextMove Software Ltd. 1 INTRODUCTION ....................................................................................................................... 5 1.1 StarDrop overview ............................................................................................................................. 5 1.2 Reference guide summary ............................................................................................................... 8 2 PROBABILISTIC SCORING .................................................................................................. 10 2.1 Defining scoring criteria ................................................................................................................. 10 2.2 Importance of uncertainty ............................................................................................................. 14 2.3 Interpreting scores .......................................................................................................................... 16 3 CHEMICAL SPACE AND COMPOUND SELECTION .......................................................... 18 3.1 Introduction ..................................................................................................................................... 18 3.2 Chemical similarity and diversity ................................................................................................... 18 3.3 Visualising chemical space ............................................................................................................. 20 3.4 Selecting compounds ...................................................................................................................... 23 3.5 Limitations and tips ......................................................................................................................... 25 4 GLOWING MOLECULE™ ........................................................................................................ 26 4.1 Introduction ..................................................................................................................................... 26 4.2 Interpretation ................................................................................................................................... 30 5 CHEMINFORMATICS ALGORITHMS .................................................................................. 35 5.1 Clustering ......................................................................................................................................... 35 5.2 Molecular Matched Pair Analysis ................................................................................................... 36 5.3 Activity Landscapes and Cliffs ....................................................................................................... 37 6 ADME QSAR MODELS ............................................................................................................ 38 6.1 Modelling principles ......................................................................................................................... 38 6.2 Data sets........................................................................................................................................... 39 6.3 Descriptors ....................................................................................................................................... 39 6.4 Fitting methods ................................................................................................................................ 40 6.5 Validation .......................................................................................................................................... 42 6.6 Chemical space ................................................................................................................................ 43 6.7 Interpreting model results ............................................................................................................. 44 6.8 Global versus local models ............................................................................................................. 44 7 P450 METABOLISM MODELS .............................................................................................. 45 2 7.1 Introduction to P450 metabolism ................................................................................................. 45 7.2 Modelling principles ......................................................................................................................... 46 7.3 Interpreting model results ............................................................................................................. 49 7.4 Model Performance ......................................................................................................................... 54 8 AUTO-MODELLER™ ................................................................................................................ 55 8.1 Introduction ..................................................................................................................................... 55 8.2 Descriptors ....................................................................................................................................... 56 8.3 Data set preparation ....................................................................................................................... 56 8.4 Modelling techniques ...................................................................................................................... 57 8.5 Gaussian Processes ......................................................................................................................... 59 8.6 Radial Basis Functions with a Genetic Algorithm ........................................................................ 62 8.7 Partial Least Squares ...................................................................................................................... 68 8.8 Decision Trees ................................................................................................................................. 69 8.9 Random Forests ............................................................................................................................... 73 8.10 Confidence in prediction ................................................................................................................. 74 9 MPO EXPLORER™ ................................................................................................................... 75 9.1 Profile Builder ................................................................................................................................... 75 9.2 Sensitivity Analysis .......................................................................................................................... 81 10 NOVA™ IDEA GENERATION ................................................................................................ 86 10.1 Introduction ..................................................................................................................................... 86 10.2 Medicinal Chemistry Transformations .......................................................................................... 86 10.3 Matched Series Analysis ................................................................................................................. 94 11 BIOSTER™ .............................................................................................................................. 100 11.1 The BIOSTER database ................................................................................................................ 100 11.2 Creating Bioisosteric Transformations ........................................................................................ 100 11.3 Predictive Application of Bioisosteric Transformations ............................................................ 102 11.4 Conclusions .................................................................................................................................... 105 12 TORCH3D™ ............................................................................................................................ 106 12.1 Introduction ................................................................................................................................... 106 12.2 What are Field Points? .................................................................................................................. 107 12.3 Interpretation of Field Point Patterns ......................................................................................... 107 12.4 Reference molecules ..................................................................................................................... 108 3 12.5 torch3D Scores .............................................................................................................................. 108 13 DEREK NEXUS™ .................................................................................................................... 109 13.1 Derek Endpoint Descriptions ......................................................................................................
Load more

Recommended publications
  • A Phase I Trial of Tamoxifen with Ribociclib (LEE011) in Adult Patients with Advanced ER+ (HER2 Negative) Breast Cancer
    The TEEL Study: A Phase I Trial of Tamoxifen with Ribociclib (LEE011) in Adult Patients with Advanced ER+ (HER2 Negative) Breast Cancer NCT02586675 Version 12.0 September 14, 2016 TEEL Protocol- Tamoxifen +Ribociclib Page 1 TITLE PAGE The TEEL Study: A Phase I trial of Tamoxifen with Ribociclib (LEE011) in adult patients with advanced ER+ (HER2 negative) breast cancer. Protocol: MCC 18332 Chesapeake IRB Pro00015228 Principal Investigator: Co-Investigators: Statistician: Experimental Therapeutics Program H. Lee Moffitt Cancer Center 12902 Magnolia Drive Tampa, FL 33612 & Comprehensive Breast Program Moffitt McKinley Outpatient Center 10920 N. McKinley Dr. Tampa, FL 33612 Study Site Contact: Protocol Version 12 Date: September 14, 2016 TEEL Protocol- Tamoxifen +Ribociclib Page 2 TITLE PAGE .............................................................................................................................................. 1 SYNOPSIS ................................................................................................................................................... 5 Patient Population ................................................................................................................................. 5 Type of Study ......................................................................................................................................... 5 Prior Therapy......................................................................................................................................... 5
    [Show full text]
  • Drug Development for the Irritable Bowel Syndrome: Current Challenges and Future Perspectives
    REVIEW ARTICLE published: 01 February 2013 doi: 10.3389/fphar.2013.00007 Drug development for the irritable bowel syndrome: current challenges and future perspectives Fabrizio De Ponti* Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy Edited by: Medications are frequently used for the treatment of patients with the irritable bowel syn- Angelo A. Izzo, University of Naples drome (IBS), although their actual benefit is often debated. In fact, the recent progress in Federico II, Italy our understanding of the pathophysiology of IBS, accompanied by a large number of preclin- Reviewed by: Elisabetta Barocelli, University of ical and clinical studies of new drugs, has not been matched by a significant improvement Parma, Italy of the armamentarium of medications available to treat IBS. The aim of this review is to Raffaele Capasso, University of outline the current challenges in drug development for IBS, taking advantage of what we Naples Federico II, Italy have learnt through the Rome process (Rome I, Rome II, and Rome III). The key questions *Correspondence: that will be addressed are: (a) do we still believe in the “magic bullet,” i.e., a very selective Fabrizio De Ponti, Pharmacology Unit, Department of Medical and Surgical drug displaying a single receptor mechanism capable of controlling IBS symptoms? (b) IBS Sciences, University of Bologna, Via is a “functional disorder” where complex neuroimmune and brain-gut interactions occur Irnerio, 48, 40126 Bologna, Italy. and minimal inflammation is often documented:
    [Show full text]
  • The Science of Medicine. the Art of Healing. Going Lecture Free for Genz
    The science of medicine. The art of healing. Going Lecture Free for GenZ Brenda J.B. Roman, M.D. Associate Dean for Medical Education Irina Overman, M.D. Director of Foundations Curriculum Mary Jo Trout, Pharm.D. Director of Therapeutics Curriculum Image obtained from: https://visual.ly/community/infographic/other/generation-z Gen Z Learning Preferences ▪ “Hands on” learning opportunities ▪ Application to “real Life” ▪ Desire community engagement ▪ “Observers” first ▪ Value independent learning ▪ Differs from Millennials (teamwork-oriented approach) ▪ View peers and instructors as valuable resources No attendance problems What we did… Science of Learning Research… What doesn’t work Cognitive illusions ▪ Ineffective strategies that produce massive overconfidence Popular ineffective strategies ▪ Passive repetitive reading ▪ Highlighting and underlining ▪ Summarization ▪ Keyword mnemonics ▪ Imagery for text Science of Learning Research… What does work ▪ Interleaved practice ▪ Elaborative interrogation ▪ Self-explanation ▪ Distributed practice ▪ Practice testing ▪ Retrieval-based learning Foster a Growth Mindset ➢ Willingness to learn ➢ Get comfortable with from reading less than full mastery of material before the session Peer Multiple-Choice Instruction Questions Team-Based WrightQ Learning WrightCurriculum DISTRIBUTED PRACTICE INTERLEAVING READING Peer Instruction Students answer problem sets individually with ARS, then peer instruct and re-answer individually Retrieval-based learning Elaborative interrogation Self-explanation Practice
    [Show full text]
  • (12) United States Patent (10) Patent No.: US 9,498,481 B2 Rao Et Al
    USOO9498481 B2 (12) United States Patent (10) Patent No.: US 9,498,481 B2 Rao et al. (45) Date of Patent: *Nov. 22, 2016 (54) CYCLOPROPYL MODULATORS OF P2Y12 WO WO95/26325 10, 1995 RECEPTOR WO WO99/O5142 2, 1999 WO WOOO/34283 6, 2000 WO WO O1/92262 12/2001 (71) Applicant: Apharaceuticals. Inc., La WO WO O1/922.63 12/2001 olla, CA (US) WO WO 2011/O17108 2, 2011 (72) Inventors: Tadimeti Rao, San Diego, CA (US); Chengzhi Zhang, San Diego, CA (US) OTHER PUBLICATIONS Drugs of the Future 32(10), 845-853 (2007).* (73) Assignee: Auspex Pharmaceuticals, Inc., LaJolla, Tantry et al. in Expert Opin. Invest. Drugs (2007) 16(2):225-229.* CA (US) Wallentin et al. in the New England Journal of Medicine, 361 (11), 1045-1057 (2009).* (*) Notice: Subject to any disclaimer, the term of this Husted et al. in The European Heart Journal 27, 1038-1047 (2006).* patent is extended or adjusted under 35 Auspex in www.businesswire.com/news/home/20081023005201/ U.S.C. 154(b) by Od en/Auspex-Pharmaceuticals-Announces-Positive-Results-Clinical M YW- (b) by ayS. Study (published: Oct. 23, 2008).* This patent is Subject to a terminal dis- Concert In www.concertpharma. com/news/ claimer ConcertPresentsPreclinicalResultsNAMS.htm (published: Sep. 25. 2008).* Concert2 in Expert Rev. Anti Infect. Ther. 6(6), 782 (2008).* (21) Appl. No.: 14/977,056 Springthorpe et al. in Bioorganic & Medicinal Chemistry Letters 17. 6013-6018 (2007).* (22) Filed: Dec. 21, 2015 Leis et al. in Current Organic Chemistry 2, 131-144 (1998).* Angiolillo et al., Pharmacology of emerging novel platelet inhibi (65) Prior Publication Data tors, American Heart Journal, 2008, 156(2) Supp.
    [Show full text]
  • United States Patent (10) Patent No.: US 8,969,514 B2 Shailubhai (45) Date of Patent: Mar
    USOO896.9514B2 (12) United States Patent (10) Patent No.: US 8,969,514 B2 Shailubhai (45) Date of Patent: Mar. 3, 2015 (54) AGONISTS OF GUANYLATECYCLASE 5,879.656 A 3, 1999 Waldman USEFUL FOR THE TREATMENT OF 36; A 6. 3: Watts tal HYPERCHOLESTEROLEMIA, 6,060,037- W - A 5, 2000 Waldmlegand et al. ATHEROSCLEROSIS, CORONARY HEART 6,235,782 B1 5/2001 NEW et al. DISEASE, GALLSTONE, OBESITY AND 7,041,786 B2 * 5/2006 Shailubhai et al. ........... 530.317 OTHER CARDOVASCULAR DISEASES 2002fOO78683 A1 6/2002 Katayama et al. 2002/O12817.6 A1 9/2002 Forssmann et al. (75) Inventor: Kunwar Shailubhai, Audubon, PA (US) 2003,2002/0143015 OO73628 A1 10/20024, 2003 ShaubhaiFryburg et al. 2005, OO16244 A1 1/2005 H 11 (73) Assignee: Synergy Pharmaceuticals, Inc., New 2005, OO32684 A1 2/2005 Syer York, NY (US) 2005/0267.197 A1 12/2005 Berlin 2006, OO86653 A1 4, 2006 St. Germain (*) Notice: Subject to any disclaimer, the term of this 299;s: A. 299; NS et al. patent is extended or adjusted under 35 2008/0137318 A1 6/2008 Rangarajetal.O U.S.C. 154(b) by 742 days. 2008. O151257 A1 6/2008 Yasuda et al. 2012/O196797 A1 8, 2012 Currie et al. (21) Appl. No.: 12/630,654 FOREIGN PATENT DOCUMENTS (22) Filed: Dec. 3, 2009 DE 19744O27 4f1999 (65) Prior Publication Data WO WO-8805306 T 1988 WO WO99,26567 A1 6, 1999 US 2010/O152118A1 Jun. 17, 2010 WO WO-0 125266 A1 4, 2001 WO WO-02062369 A2 8, 2002 Related U.S.
    [Show full text]
  • AHFS Pharmacologic-Therapeutic Classification System
    AHFS Pharmacologic-Therapeutic Classification System Abacavir 48:24 - Mucolytic Agents - 382638 8:18.08.20 - HIV Nucleoside and Nucleotide Reverse Acitretin 84:92 - Skin and Mucous Membrane Agents, Abaloparatide 68:24.08 - Parathyroid Agents - 317036 Aclidinium Abatacept 12:08.08 - Antimuscarinics/Antispasmodics - 313022 92:36 - Disease-modifying Antirheumatic Drugs - Acrivastine 92:20 - Immunomodulatory Agents - 306003 4:08 - Second Generation Antihistamines - 394040 Abciximab 48:04.08 - Second Generation Antihistamines - 394040 20:12.18 - Platelet-aggregation Inhibitors - 395014 Acyclovir Abemaciclib 8:18.32 - Nucleosides and Nucleotides - 381045 10:00 - Antineoplastic Agents - 317058 84:04.06 - Antivirals - 381036 Abiraterone Adalimumab; -adaz 10:00 - Antineoplastic Agents - 311027 92:36 - Disease-modifying Antirheumatic Drugs - AbobotulinumtoxinA 56:92 - GI Drugs, Miscellaneous - 302046 92:20 - Immunomodulatory Agents - 302046 92:92 - Other Miscellaneous Therapeutic Agents - 12:20.92 - Skeletal Muscle Relaxants, Miscellaneous - Adapalene 84:92 - Skin and Mucous Membrane Agents, Acalabrutinib 10:00 - Antineoplastic Agents - 317059 Adefovir Acamprosate 8:18.32 - Nucleosides and Nucleotides - 302036 28:92 - Central Nervous System Agents, Adenosine 24:04.04.24 - Class IV Antiarrhythmics - 304010 Acarbose Adenovirus Vaccine Live Oral 68:20.02 - alpha-Glucosidase Inhibitors - 396015 80:12 - Vaccines - 315016 Acebutolol Ado-Trastuzumab 24:24 - beta-Adrenergic Blocking Agents - 387003 10:00 - Antineoplastic Agents - 313041 12:16.08.08 - Selective
    [Show full text]
  • 2D6 Substrates 2D6 Inhibitors 2D6 Inducers
    Physician Guidelines: Drugs Metabolized by Cytochrome P450’s 1 2D6 Substrates Acetaminophen Captopril Dextroamphetamine Fluphenazine Methoxyphenamine Paroxetine Tacrine Ajmaline Carteolol Dextromethorphan Fluvoxamine Metoclopramide Perhexiline Tamoxifen Alprenolol Carvedilol Diazinon Galantamine Metoprolol Perphenazine Tamsulosin Amiflamine Cevimeline Dihydrocodeine Guanoxan Mexiletine Phenacetin Thioridazine Amitriptyline Chloropromazine Diltiazem Haloperidol Mianserin Phenformin Timolol Amphetamine Chlorpheniramine Diprafenone Hydrocodone Minaprine Procainamide Tolterodine Amprenavir Chlorpyrifos Dolasetron Ibogaine Mirtazapine Promethazine Tradodone Aprindine Cinnarizine Donepezil Iloperidone Nefazodone Propafenone Tramadol Aripiprazole Citalopram Doxepin Imipramine Nifedipine Propranolol Trimipramine Atomoxetine Clomipramine Encainide Indoramin Nisoldipine Quanoxan Tropisetron Benztropine Clozapine Ethylmorphine Lidocaine Norcodeine Quetiapine Venlafaxine Bisoprolol Codeine Ezlopitant Loratidine Nortriptyline Ranitidine Verapamil Brofaramine Debrisoquine Flecainide Maprotline olanzapine Remoxipride Zotepine Bufuralol Delavirdine Flunarizine Mequitazine Ondansetron Risperidone Zuclopenthixol Bunitrolol Desipramine Fluoxetine Methadone Oxycodone Sertraline Butylamphetamine Dexfenfluramine Fluperlapine Methamphetamine Parathion Sparteine 2D6 Inhibitors Ajmaline Chlorpromazine Diphenhydramine Indinavir Mibefradil Pimozide Terfenadine Amiodarone Cimetidine Doxorubicin Lasoprazole Moclobemide Quinidine Thioridazine Amitriptyline Cisapride
    [Show full text]
  • Conivaptan (Vaprisol )
    January 3, 2007 Conivaptan (Vaprisol ®) DESCRIPTION WHAT YOU MAY NOT BE TOLD Conivaptan (Vaprisol ®) is an inhibitor of arginine Conivaptan is currently approved for euvolemic vasopressin that prevents its binding to V1a and V2 hyponatremia, but the only peer-reviewed published receptors located within the vasculature and renal data is in acute exacerbations of heart failure. It is tubules, respectively. Conivaptan was approved likely that the drug manufacturer will pursue this December 30, 2005, and is a first-in-class agent indication in the future. Conivaptan is also a potent approved for euvolemic hyponatremia. Conivaptan is inhibitor of CYP 3A4 and, as an oral formulation, available in 20 mg/5mL glass ampules. increases the area under the curve of midazolam 2- to 3-fold, simvastatin 3-fold, and amlodipine 2-fold. This WHAT THE PHARMACIST SHOULD KNOW level of interaction is what prevented the oral Euvolemic hyponatremia is most commonly associated formulation from being pursued for FDA approval. with the syndrome of inappropriate antidiuretic hormone secretion (SIADH). Causes of this syndrome WHAT THE PATIENT SHOULD KNOW include pulmonary disease (e.g., pneumonia, Conivaptan is used to treat euvolemic hyponatremia, tuberculosis, pleural effusion) and CNS disease (e.g., manifested when a patient has too little sodium in the tumor, subarachnoid hemorrhage, meningitis). SIADH bloodstream but a normal amount of fluid in the body. also occurs with malignancies (e.g., small cell Conivaptan should not be used at this time for heart carcinoma of the lung) and can be caused by drugs failure. Conivaptan has relatively few adverse effects (e.g., chlorpropamide, carbamazepine, narcotic and is well tolerated.
    [Show full text]
  • NTP-CERHR Expert Panel Report on the Reproductive and Developmental Toxicity of Amphetamine and Methamphetamine
    Published 2005 Wiley-Liss, Inc.w Birth Defects Research (Part B) 74:471–584 (2005) NTP-CERHR Expert Panel Report on the Reproductive and Developmental Toxicity Of Amphetamine and Methamphetamine Mari Golub,1 Lucio Costa,2 Kevin Crofton,3 Deborah Frank,4 Peter Fried,5 Beth Gladen6 Rogene Henderson,7 Erica Liebelt,8 Shari Lusskin,9 Sue Marty,10 Andrew Rowland11 John Scialli12 and Mary Vore13 1California Environment Protection Agency, Sacramento, California 2University of Washington, Seattle, Washington 3U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 4Boston Medical Center, Boston, Massachusetts 5Carleton University, Ottawa, Ontario 6National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 7Lovelace Respiratory Research Institute, Albuquerque, New Mexico 8University of Alabama at Birmingham School of Medicine, Birmingham, Alabama 9New York University School of Medicine, New York, New York 10The Dow Chemical Company, Midland, Michigan 11University of New Mexico, Albuquerque, New Mexico 12Phoenix, Arizona 13University of Kentucky, Lexington, Kentucky PREFACE studies indexed before December 31, 2004. References were also identified from databases such as REPRO- The National Toxicology Program (NTP) and the TOXs, HSDB, IRIS, and DART and from report National Institute of Environmental Health Sciences bibliographies. (NIEHS) established the NTP Center for the Evaluation This evaluation resulted from the efforts of a 13- of Risks to Human Reproduction (CERHR) in June 1998. member panel of government and non-government The purpose of the Center is to provide timely, unbiased, scientists that culminated in a public expert panel scientifically sound evaluations of human and experi- meeting held January 10–12, 2005. This report is a mental evidence for adverse effects on reproduction and product of the Expert Panel and is intended to (1) development caused by agents to which humans may be interpret the strength of scientific evidence that exposed.
    [Show full text]
  • Genetically Modified Baculoviruses for Pest
    INSECT CONTROL BIOLOGICAL AND SYNTHETIC AGENTS This page intentionally left blank INSECT CONTROL BIOLOGICAL AND SYNTHETIC AGENTS EDITED BY LAWRENCE I. GILBERT SARJEET S. GILL Amsterdam • Boston • Heidelberg • London • New York • Oxford Paris • San Diego • San Francisco • Singapore • Sydney • Tokyo Academic Press is an imprint of Elsevier Academic Press, 32 Jamestown Road, London, NW1 7BU, UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA ª 2010 Elsevier B.V. All rights reserved The chapters first appeared in Comprehensive Molecular Insect Science, edited by Lawrence I. Gilbert, Kostas Iatrou, and Sarjeet S. Gill (Elsevier, B.V. 2005). All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publishers. Permissions may be sought directly from Elsevier’s Rights Department in Oxford, UK: phone (þ44) 1865 843830, fax (þ44) 1865 853333, e-mail [email protected]. Requests may also be completed on-line via the homepage (http://www.elsevier.com/locate/permissions). Library of Congress Cataloging-in-Publication Data Insect control : biological and synthetic agents / editors-in-chief: Lawrence I. Gilbert, Sarjeet S. Gill. – 1st ed. p. cm. Includes bibliographical references and index. ISBN 978-0-12-381449-4 (alk. paper) 1. Insect pests–Control. 2. Insecticides. I. Gilbert, Lawrence I. (Lawrence Irwin), 1929- II. Gill, Sarjeet S. SB931.I42 2010 632’.7–dc22 2010010547 A catalogue record for this book is available from the British Library ISBN 978-0-12-381449-4 Cover Images: (Top Left) Important pest insect targeted by neonicotinoid insecticides: Sweet-potato whitefly, Bemisia tabaci; (Top Right) Control (bottom) and tebufenozide intoxicated by ingestion (top) larvae of the white tussock moth, from Chapter 4; (Bottom) Mode of action of Cry1A toxins, from Addendum A7.
    [Show full text]
  • )&F1y3x PHARMACEUTICAL APPENDIX to THE
    )&f1y3X PHARMACEUTICAL APPENDIX TO THE HARMONIZED TARIFF SCHEDULE )&f1y3X PHARMACEUTICAL APPENDIX TO THE TARIFF SCHEDULE 3 Table 1. This table enumerates products described by International Non-proprietary Names (INN) which shall be entered free of duty under general note 13 to the tariff schedule. The Chemical Abstracts Service (CAS) registry numbers also set forth in this table are included to assist in the identification of the products concerned. For purposes of the tariff schedule, any references to a product enumerated in this table includes such product by whatever name known. Product CAS No. Product CAS No. ABAMECTIN 65195-55-3 ACTODIGIN 36983-69-4 ABANOQUIL 90402-40-7 ADAFENOXATE 82168-26-1 ABCIXIMAB 143653-53-6 ADAMEXINE 54785-02-3 ABECARNIL 111841-85-1 ADAPALENE 106685-40-9 ABITESARTAN 137882-98-5 ADAPROLOL 101479-70-3 ABLUKAST 96566-25-5 ADATANSERIN 127266-56-2 ABUNIDAZOLE 91017-58-2 ADEFOVIR 106941-25-7 ACADESINE 2627-69-2 ADELMIDROL 1675-66-7 ACAMPROSATE 77337-76-9 ADEMETIONINE 17176-17-9 ACAPRAZINE 55485-20-6 ADENOSINE PHOSPHATE 61-19-8 ACARBOSE 56180-94-0 ADIBENDAN 100510-33-6 ACEBROCHOL 514-50-1 ADICILLIN 525-94-0 ACEBURIC ACID 26976-72-7 ADIMOLOL 78459-19-5 ACEBUTOLOL 37517-30-9 ADINAZOLAM 37115-32-5 ACECAINIDE 32795-44-1 ADIPHENINE 64-95-9 ACECARBROMAL 77-66-7 ADIPIODONE 606-17-7 ACECLIDINE 827-61-2 ADITEREN 56066-19-4 ACECLOFENAC 89796-99-6 ADITOPRIM 56066-63-8 ACEDAPSONE 77-46-3 ADOSOPINE 88124-26-9 ACEDIASULFONE SODIUM 127-60-6 ADOZELESIN 110314-48-2 ACEDOBEN 556-08-1 ADRAFINIL 63547-13-7 ACEFLURANOL 80595-73-9 ADRENALONE
    [Show full text]
  • Inhibition of Monoamine Oxidase in 5
    Br. J. Pharmac. (1985), 85, 683-690 Inhibition ofmonoamine oxidase in 5- hydroxytryptaminergic neurones by substitutedp- aminophenylalkylamines Anna-Lena Ask, Ingrid Fagervall, L. Florvall, S.B. Ross1 & Susanne Ytterborn Research Laboratories, Astra Likemedel AB, S-151 85 Si3dertilje, Sweden 1 A series ofsubstituted p-aminophenethylamines and some related compounds were examined with regards to the inhibition ofmonoamine oxidase (MAO) in vivo inside and outside 5-hydroxytryptamin- ergic neurones in the rat hypothalamus. This was recorded as the protection against the irreversible inhibition of MAO produced by phenelzine by determining the remaining deaminating activity in the absence and presence ofcitalopram using a low (0.1 yIM) concentration of ['4CJ-5-hydroxytryptamine (5-HT) as substrate. 2 Some ofthe phenethylamines were much more potent inside than outside the 5-hydroxytryptamin- ergic neurones. This neuronal selectivity was antagonized by pretreatment of the rats with norzimeldine, a 5-HT uptake inhibitor, which indicates that these compounds are accumulated in the 5-HT nerve terminals by the 5-HT pump. 3 Selectivity was obtained for compounds with dimethyl, monomethyl or unsubstituted p-amino groups. An isopropyl group appears to substitute for the dimethylamino group but with considerably lower potency. Compounds with 2-substitution showed selectivity for aminergic neurones and this effect decreased with increased size of the substituent. The 2,6-dichloro derivative FLA 365 had, however, no neuronal selective action but was a potent MAO inhibitor. Substitutions in the 3- and 5- positions decreased both potency and selectivity. 4 Prolongation ofthe side chain with one methylene group abolished the preference for the MAO in 5-hydroxytryptaminergic neurones although the MAO inhibitory potency remained.
    [Show full text]