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Ion Channels in Drug Discovery
Department of Physiology and Pharmacology Karolinska Institutet, Stockholm, Sweden Thesis for doctoral degree (Ph.D.) 2008 Thesis for doctoral degree (Ph.D.) 2008 Ion Channels in Drug Discovery - focus on biological assays Ion Channels in Drug Discovery - focus on biological assays Kim Dekermendjian Ion Channels in Drug Discovery - focus on biological - focus on in Drug assays Channels Discovery Ion Kim Dekermendjian Kim Dekermendjian Stockholm 2008 Department of Physiology and Pharmacology Karolinska Institutet, Stockholm, Sweden Ion Channels in Drug Discovery - focus on biological assays Kim Dekermendjian Stockholm 2008 Main Supervisor: Bertil B Fredholm, Professor Department of Physiology and Pharmacology Karolinska Institutet. Co-Supervisor: Olof Larsson, PhD Associated Director Department of Molecular Pharmacology, Astrazeneca R&D Södertälje. Opponent: Bjarke Ebert, Adjunct Professor Faculty of Pharmaceutical Sciences, University of Copenhagen, Denmark. and Department of Electrophysiology, H. Lundbeck A/S, Copenhagen, Denmark. Examination board: Ole Kiehn, Professor Department of Neurovetenskap, Karolinska Institutet. Bryndis Birnir, PhD Docent Department of Clinical Sciences, Lund University. Bo Rydqvist, Professor Department of Physiology and Pharmacology Karolinska Institutet. All previously published papers were reproduced with permission from the publisher. Front page (a) Side view of the GABA-A model; the trans membrane domain (TMD) (bottom) spans through the lipid membrane (32 Å) and partly on the extracellular side (12 Å), while the ligand binding domain (LBD) (top) is completely extracellular (60 Å). (b) The LBD viewed from the extracellular side (diameter of 80 Å). (c) The TMD viewed from the extracellular side. Arrows and ribbons (which correspond to ȕ-strands and Į- helices, respectively) are coloured according to position in each individual subunit sequence, as well as in the TM4 of each subunit which is not bonded to the rest of the model (with permission from Campagna-Slater and Weaver, 2007 Copyright © 2006 Elsevier Inc.). -
Exploring Non-Covalent Interactions Between Drug-Like Molecules and the Protein Acetylcholinesterase
Exploring non-covalent interactions between drug-like molecules and the protein acetylcholinesterase Lotta Berg Doctoral Thesis, 2017 Department of Chemistry Umeå University, Sweden This work is protected by the Swedish Copyright Legislation (Act 1960:729) ISBN: 978-91-7601-644-2 Elektronisk version tillgänglig på http://umu.diva-portal.org/ Printed by: VMC-KBC Umeå Umeå, Sweden, 2017 “Life is a relationship between molecules, not a property of any one molecule” Emile Zuckerkandl and Linus Pauling 1962 i Table of contents Abstract vi Sammanfattning vii Papers covered in the thesis viii List of abbreviations x 1. Introduction 1 1.1. Drug discovery 1 1.2. The importance of non-covalent interactions 1 1.3. Fundamental forces of non-covalent interactions 2 1.4. Classes of non-covalent interactions 3 1.5. Hydrogen bonds 3 1.5.1. Definition of the hydrogen bond 3 1.5.2. Classical and non-classical hydrogen bonds 4 1.5.3. CH∙∙∙Y hydrogen bonds 5 1.6. Interactions between aromatic rings 6 1.7. Thermodynamics of binding 7 1.8. The essential enzyme acetylcholinesterase 8 1.8.1. The relevance of research focused on AChE 8 1.8.2. The structure of AChE 9 1.8.3. AChE in drug discovery 10 2. Scope of the thesis 12 3. Background to techniques and methods 13 3.1. Structure determination by X-ray crystallography 13 3.1.1. The significance of crystal structures of proteins 13 3.1.2. Data collection and structure refinement 13 3.1.3. Electron density maps 14 3.1.4. -
(12) United States Patent (10) Patent No.: US 9,161,992 B2 Jefferies Et Al
US009 161992B2 (12) United States Patent (10) Patent No.: US 9,161,992 B2 Jefferies et al. (45) Date of Patent: Oct. 20, 2015 (54) P97 FRAGMENTS WITH TRANSFER 4,683.202 A 7, 1987 Mullis ACTIVITY 4,704,362 A 11/1987 Itakura et al. 4,766,075 A 8, 1988 Goeddeletal. (71) Applicant: biosis Technologies, Inc., Richmond 4,800,1594,784.950 A 11/19881/1989 MullisHagen et al. (CA) 4,801,542 A 1/1989 Murray et al. 4.866,042 A 9, 1989 Neuwelt (72) Inventors: Wilfred Jefferies, South Surrey (CA); 4,935,349 A 6/1990 McKnight et al. Mei Mei Tian, Coquitlam (CA): 4.946,778 A 8, 1990 Ladner et al. Timothy Vitalis, Vancouver (CA) 5,091,513 A 2f1992 Huston et al. 5,132,405 A 7, 1992 Huston et al. (73) Assignee: biOasis Technologies, Inc., British 5, 186,941 A 2f1993 Callahan et al. Columbia (CA) 5,672,683 A 9, 1997 Friden et al. 5,677,171 A 10, 1997 Hudziak et al. c - r 5,720,937 A 2f1998 Hudziak et al. (*) Notice: Subject to any disclaimer, the term of this 5,720,954. A 2f1998 Hudziak et al. patent is extended or adjusted under 35 5,725,856 A 3, 1998 Hudziak et al. U.S.C. 154(b) by 0 days. 5,770,195 A 6/1998 Hudziak et al. 5,772,997 A 6/1998 Hudziak et al. (21) Appl. No.: 14/226,506 5,844,093 A 12/1998 Kettleborough et al. 5,962,012 A 10, 1999 Lin et al. -
Clinical Pharmacology of Antiмcancer Agents
A CLINI CLINICAL PHARMACOLOGY OF NTI- C ANTI-CANCER AGENTS C AL PHARMA AN Cristiana Sessa, Luca Gianni, Marina Garassino, Henk van Halteren www.esmo.org C ER A This is a user-friendly handbook, designed for young GENTS medical oncologists, where they can access the essential C information they need for providing the treatment which could OLOGY OF have the highest chance of being effective and tolerable. By finding out more about pharmacology from this handbook, young medical oncologists will be better able to assess the different options available and become more knowledgeable in the evaluation of new treatments. ESMO ESMO Handbook Series European Society for Medical Oncology CLINICAL PHARMACOLOGY OF Via Luigi Taddei 4, 6962 Viganello-Lugano, Switzerland Handbook Series ANTI-CANCER AGENTS Cristiana Sessa, Luca Gianni, Marina Garassino, Henk van Halteren ESMO Press · ISBN 978-88-906359-1-5 ISBN 978-88-906359-1-5 www.esmo.org 9 788890 635915 ESMO Handbook Series handbook_print_NEW.indd 1 30/08/12 16:32 ESMO HANDBOOK OF CLINICAL PHARMACOLOGY OF ANTI-CANCER AGENTS CM15 ESMO Handbook 2012 V14.indd1 1 2/9/12 17:17:35 ESMO HANDBOOK OF CLINICAL PHARMACOLOGY OF ANTI-CANCER AGENTS Edited by Cristiana Sessa Oncology Institute of Southern Switzerland, Bellinzona, Switzerland; Ospedale San Raffaele, IRCCS, Unit of New Drugs and Innovative Therapies, Department of Medical Oncology, Milan, Italy Luca Gianni Ospedale San Raffaele, IRCCS, Unit of New Drugs and Innovative Therapies, Department of Medical Oncology, Milan, Italy Marina Garassino Oncology Department, Istituto Nazionale dei Tumori, Milan, Italy Henk van Halteren Department of Internal Medicine, Gelderse Vallei Hospital, Ede, The Netherlands ESMO Press CM15 ESMO Handbook 2012 V14.indd3 3 2/9/12 17:17:35 First published in 2012 by ESMO Press © 2012 European Society for Medical Oncology All rights reserved. -
Biological Target and Its Mechanism He Zuan* Department of Pharmacology, School of Medicine,Shenzhen University, Shenzhen 518000, China
OPEN ACCESS Freely available online Journal of Cell Signaling Editorial Biological Target and Its Mechanism He Zuan* Department of Pharmacology, School of Medicine,Shenzhen University, Shenzhen 518000, China EDITORIAL NOTE 1. There is no direct change in the biological target, but the binding of the substance stops other endogenous substances (such Biological target is whatsoever within an alive organism to which as triggering hormones) from binding to the target. Contingent some other entity (like an endogenous drug or ligand) is absorbed on the nature of the target, this effect is mentioned as receptor and binds, subsequent in a modification in its function or antagonism, ion channel blockade or enzyme inhibition. behaviour. For examples of mutual biological targets are nucleic acids and proteins. Definition is context dependent, and can refer 2. A conformational change in the target is induced by the stimulus to the biological target of a pharmacologically active drug multiple, which results in a change in target function. This change in function the receptor target of a hormone like insulin, or some other target can mimic the effect of the endogenous substance in which case of an external stimulus. Biological targets are most commonly the effect is referred to as receptor agonism or be the opposite of proteins such as ion channels, enzymes, and receptors. The term the endogenous substance which in the case of receptors is referred "biological target" is regularly used in research of pharmaceutical to as inverse agonism. to describe the nastive protein in body whose activity is altered Conservation Ecology: These biological targets are conserved by a drug subsequent in a specific effect, which may a desirable across species, making pharmaceutical pollution of the therapeutic effect or an unwanted adverse effect. -
Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks
bioRxiv preprint doi: https://doi.org/10.1101/2020.03.12.988600; this version posted March 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Uncovering new drug properties in target-based drug-drug similarity networks Lucret¸ia Udrescu1, Paul Bogdan2, Aim´eeChi¸s3, Ioan Ovidiu S^ırbu3,6, Alexandru Top^ırceanu4, Renata-Maria V˘arut¸5, Mihai Udrescu4,6* 1 Department of Drug Analysis, "Victor Babe¸s",University of Medicine and Pharmacy Timi¸soara,Timi¸soara,Romania 2 Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA 3 Department of Biochemistry, "Victor Babe¸s"University of Medicine and Pharmacy Timi¸soara,Timi¸soara,Romania 4 Department of Computer and Information Technology, Politehnica University of Timi¸soara,Timi¸soara,Romania 5 Faculty of Pharmacy, University of Medicine and Pharmacy of Craiova, Craiova, Romania 6 Timi¸soaraInstitute of Complex Systems, Timi¸soara,Romania * [email protected] Abstract Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach { based on knowledge about the chemical structures { cannot fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug-target interactions to infer drug properties. -
MDMA, MBDB' and I! I the Classichallucinogens
Differences Between the Mechanism I t of Action of MDMA, MBDB' and i! I the ClassicHallucinogens. i Identification of a New Therapeutic Class: Entactogens Jawi'ri", ! 7 U$ DAVID E. NICHOLS, PH.D.* The widespread use of psychedelic drugs, such as quantitative structure-activity relationships (QSARs). lysergic ac;.d diethylamide (LSD), during the 1960's and Simply stated, attempts are made to find quantitative 1970's IcC to severe reactions by governmental agencies correlations (or equations) that relate biological activity to and proscriptions against their use. However, with the fundamental properties of the molecule. This approach is high deg _e of interest in mind-altering drugs in the being widely developed in the pharmaceutical industry in United States, as evidenced by their widespread popular- order to understand more fully how particular types of ity, it wasonlyamatteroftimebeforenewdrugsappeared drugs work, and to be able to predict which additional that were developed outside of the pharmaceutical corn- molecules should be synthesized. It should also be added panics, that no equations have been developed that adequately j Nearly 70 years after its first synthesis, 3,4- correlate hallucinogenic or psychedelic activity with any methy'enedioxymethamphetamine (MDMA) was redis- particular molecular property. covered. Although it had its' more recent origin in the class In this context, it is important to define clearly the of dr. gs that is generally defined as psychedelic or hallu- type of biological activity that is being measured. It is cino enic, it clearly appears different from LSD. In hu- typical that if a molecule has several different sites of mar , MDMA induces a state of reduced anxiety and action in the brain, that each oneof these actions may be lc .red defensiveness that makes it attractive to thera- related to entirely different structural features or proper- pi . -
Next Generation Risk Assessment of Human Exposure to Anti-Androgens Using Newly Defined Comparator Compound Values
Toxicology in Vitro 73 (2021) 105132 Contents lists available at ScienceDirect Toxicology in Vitro journal homepage: www.elsevier.com/locate/toxinvit Next generation risk assessment of human exposure to anti-androgens using newly defined comparator compound values Tessa C.A. van Tongeren a,*, Thomas E. Moxon b, Matthew P. Dent b, Hequn Li b, Paul L. Carmichael b, Ivonne M.C.M. Rietjens a a Division of Toxicology, Wageningen University and Research, 6700, EA, Wageningen, the Netherlands b Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK ARTICLE INFO ABSTRACT Keywords: Next Generation Risk Assessment (NGRA) can use the so-called Dietary Comparator Ratio (DCR) to evaluate the Risk assessment safety of a definedexposure to a compound of interest. The DCR compares the Exposure Activity Ratio (EAR) for 3R compliant method the compound of interest, to the EAR of an established safe level of human exposure to a comparator compound Androgen receptor with the same putative mode of action. A DCR ≤ 1 indicates the exposure evaluated is safe. The present study Dietary comparator aimed at defining adequate and safe comparator compound exposures for evaluation of anti-androgenic effects, In vitro/in silico approaches using 3,3-diindolylmethane (DIM), from cruciferous vegetables, and the anti-androgenic drug bicalutamide (BIC). EAR values for these comparator compounds were definedusing the AR-CALUX assay. The adequacy of the new comparator EAR values was evaluated using PBK modelling and by comparing the generated DCRs of a series of test compound exposures to actual knowledge on their safety regarding in vivo anti-androgenicity. -
(12) Patent Application Publication (10) Pub. No.: US 2017/0172932 A1 Peyman (43) Pub
US 20170172932A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2017/0172932 A1 Peyman (43) Pub. Date: Jun. 22, 2017 (54) EARLY CANCER DETECTION AND A 6LX 39/395 (2006.01) ENHANCED IMMUNOTHERAPY A61R 4I/00 (2006.01) (52) U.S. Cl. (71) Applicant: Gholam A. Peyman, Sun City, AZ CPC .......... A61K 9/50 (2013.01); A61K 39/39558 (US) (2013.01); A61K 4I/0052 (2013.01); A61 K 48/00 (2013.01); A61K 35/17 (2013.01); A61 K (72) Inventor: sham A. Peyman, Sun City, AZ 35/15 (2013.01); A61K 2035/124 (2013.01) (21) Appl. No.: 15/143,981 (57) ABSTRACT (22) Filed: May 2, 2016 A method of therapy for a tumor or other pathology by administering a combination of thermotherapy and immu Related U.S. Application Data notherapy optionally combined with gene delivery. The combination therapy beneficially treats the tumor and pre (63) Continuation-in-part of application No. 14/976,321, vents tumor recurrence, either locally or at a different site, by filed on Dec. 21, 2015. boosting the patient’s immune response both at the time or original therapy and/or for later therapy. With respect to Publication Classification gene delivery, the inventive method may be used in cancer (51) Int. Cl. therapy, but is not limited to such use; it will be appreciated A 6LX 9/50 (2006.01) that the inventive method may be used for gene delivery in A6 IK 35/5 (2006.01) general. The controlled and precise application of thermal A6 IK 4.8/00 (2006.01) energy enhances gene transfer to any cell, whether the cell A 6LX 35/7 (2006.01) is a neoplastic cell, a pre-neoplastic cell, or a normal cell. -
Pharmaceutical Appendix to the Tariff Schedule 2
Harmonized Tariff Schedule of the United States (2007) (Rev. 2) Annotated for Statistical Reporting Purposes PHARMACEUTICAL APPENDIX TO THE HARMONIZED TARIFF SCHEDULE Harmonized Tariff Schedule of the United States (2007) (Rev. 2) Annotated for Statistical Reporting Purposes PHARMACEUTICAL APPENDIX TO THE TARIFF SCHEDULE 2 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. ABACAVIR 136470-78-5 ACIDUM LIDADRONICUM 63132-38-7 ABAFUNGIN 129639-79-8 ACIDUM SALCAPROZICUM 183990-46-7 ABAMECTIN 65195-55-3 ACIDUM SALCLOBUZICUM 387825-03-8 ABANOQUIL 90402-40-7 ACIFRAN 72420-38-3 ABAPERIDONUM 183849-43-6 ACIPIMOX 51037-30-0 ABARELIX 183552-38-7 ACITAZANOLAST 114607-46-4 ABATACEPTUM 332348-12-6 ACITEMATE 101197-99-3 ABCIXIMAB 143653-53-6 ACITRETIN 55079-83-9 ABECARNIL 111841-85-1 ACIVICIN 42228-92-2 ABETIMUSUM 167362-48-3 ACLANTATE 39633-62-0 ABIRATERONE 154229-19-3 ACLARUBICIN 57576-44-0 ABITESARTAN 137882-98-5 ACLATONIUM NAPADISILATE 55077-30-0 ABLUKAST 96566-25-5 ACODAZOLE 79152-85-5 ABRINEURINUM 178535-93-8 ACOLBIFENUM 182167-02-8 ABUNIDAZOLE 91017-58-2 ACONIAZIDE 13410-86-1 ACADESINE 2627-69-2 ACOTIAMIDUM 185106-16-5 ACAMPROSATE 77337-76-9 -
Using in Vitro Toxcast Assays to Evaluate Mechanistic Plausibility
Using In vitro ToxCast Assays to Evaluate Mechanistic Plausibility and Build Confidence in the Selection of Analogues for Quantitative Read-Across: A Case Study on p,p’-Dichlorodiphenyldichloroethane Lucina E. Lizarraga, Jeffry L. Dean, J. Phillip Kaiser, Scott C. Wesselkamper, Jason C. Lambert, Elizabeth O. Owens, Belinda Hawkins, Q. Jay Zhao. National Center for Environmental Assessment (NCEA), U.S. Environmental Protection Agency, Cincinnati, Ohio 45268, USA Lucina E. Lizarraga l [email protected] l 513-487 2648 Overview Bioactivity Similarity Comparisons Evaluating Mechanistic Plausibility for Liver and Reproductive Toxicity • Deriving human health reference values for environmental chemicals has traditionally relied on toxicity data from humans and/or experimental animals p,p’-DDD and Analogues Exhibit Similarities in Cell-specific Responses and Target Gene p,p’-DDD and Analogues Exhibit Similar Upregulation of Steroid/Xenobiotic-sensing Pathways in In Vitro ToxCast Assays Conducted in Human Liver Cells Nuclear Receptors in In Vitro ToxCast Assays Conducted in Hepatoma HepG2 Cells • In the absence of in vivo toxicity data, new approach methodologies such as read-across can be used to fill data gaps for a target chemical using known information from a source analogue Figure 2. Bioactivity data for p,p’-DDD and Analogues in ToxCast Assays Conducted in • A read-across approach illustrated below (Figure 1) was applied to assist in screening-level Human Hepatoma HepG2 Cells and Primary assessment of noncancer oral toxicity for the target, p,p’-DDD, a data-poor chemical known to occur Human Hepatocytes. Scatterplots show AC50 and at contaminated sites in the U.S. scaled activity values for p,p’-DDD, p,p’-DDT, p,p’- DDE and methoxychlor from in vitro assays visualized according to the type of biological response or biological target. -
WO 2016/176089 Al 3 November 2016 (03.11.2016) P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2016/176089 Al 3 November 2016 (03.11.2016) P O P C T (51) International Patent Classification: BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, A01N 43/00 (2006.01) A61K 31/33 (2006.01) DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, (21) International Application Number: KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, PCT/US2016/028383 MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, (22) International Filing Date: PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, 20 April 2016 (20.04.2016) SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every (26) Publication Language: English kind of regional protection available): ARIPO (BW, GH, (30) Priority Data: GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, 62/154,426 29 April 2015 (29.04.2015) US TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, (71) Applicant: KARDIATONOS, INC. [US/US]; 4909 DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, Lapeer Road, Metamora, Michigan 48455 (US).