DOCSLIB.ORG

Quantitative Structure-Activity Relationship Modeling to Predict Drug-Drug Interactions Between Acetaminophen and Ingredients in Energy Drinks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Quantitative Structure-Activity Relationship Modeling to Predict Drug-Drug Interactions Between Acetaminophen and Ingredients in Energy Drinks Quantitative Structure-Activity Relationship Modeling to Predict Drug-Drug Interactions Between Acetaminophen and Ingredients in Energy Drinks by Emese Somogyvari A thesis submitted to the School of Computing in conformity with the requirements for the degree of Master of Science Queen's University Kingston, Ontario, Canada August 2014 Copyright c Emese Somogyvari, 2014 Abstract The evaluation of drug-drug interactions (DDI) is a crucial step in pharmaceutical drug discovery and design. Unfortunately, if adverse effects are to occur between the co-administration of two or more drugs, they are often difficult to test for. Tradi- tional methods rely on in vitro studies as a basis for further in vivo assessment which can be a slow and costly process that may not detect all interactions. Here is pre- sented a quantitative structure-activity relationship (QSAR) modeling approach that may be used to screen drugs early in development and bring new, beneficial drugs to market more quickly and at a lesser cost. A data set of 6,532 drugs was obtained from DrugBank for which 292 QSAR descriptors were calculated. The multi-label support vector machines (SVM) method was used for classification and the K-means method was used to cluster the data. The model was validated in vitro by exposing Hepa1-6 cells to select compounds found in energy drinks and assessing cell death. Model accuracy was found to be 99%, predicting 50% of known interactions despite being biased to predicting non-interacting drug pairs. Cluster analysis revealed in- teresting information, although current progress shows that more data is needed to better analyse results, and tools that bring various drug information together would be beneficial. Non-transfected Hepa1-6 cells exposed to acetaminophen, pyridoxine, creatine, L-carnitine, taurine and caffeine did not reveal any significant drug-drug i interactions, nor were they predicted by the model. ii Co-Authorship This research was conducted by Emese Somogyvari, under the supervision of Dr. Selim G. Akl and Dr. Louise M. Winn. iii Acknowledgments I would like to thank my supervisors Dr. Selim Akl of the School of Computing, Queens University and Dr. Louise Winn of the Department of Biomedical and Molecular Sciences, Queens University for their support and guidance throughout this project, as well as faithful lab members Chrissy, Christina, Margo, Nikki, and Sam for their patience and constant assistance. iv Contents Abstract i Co-Authorship iii Acknowledgments iv Contents v List of Tables viii List of Figures x List of Abbreviations xi Chapter 1: Introduction 1 1.1 Motivation . 1 1.2 Problem . 2 1.3 Hypothesis . 2 1.4 Objectives . 3 1.5 Contributions . 3 1.5.1 QSAR modeling to predict drug-drug interactions . 3 1.5.2 Acetaminophen and ingredients in energy drinks . 4 1.6 Outline of thesis . 4 Chapter 2: Background 5 2.1 Classification QSAR modeling . 5 2.2 Classification model using support vector machines . 5 2.2.1 Preprocessing . 6 2.2.2 Classification . 8 2.3 Multi-label classification . 9 2.3.1 Problem transformation . 10 2.3.2 Algorithm adaptation . 11 v 2.4 Drug-drug interactions . 11 2.4.1 Pharmacodynamic . 12 2.4.2 Pharmacokinetic . 16 Chapter 3: Predicting Drug-Drug Interactions 20 3.1 In vitro evaluation to predict metabolism-mediated drug-drug interac- tions in vivo ................................ 20 3.1.1 Predicting P450 substrates . 21 3.1.2 Predicting P450 inducers . 21 3.1.3 Predicting P450 inhibitors . 21 3.2 In silico modeling to predict drug-drug interactions . 22 3.2.1 Classification . 22 3.2.2 Data mining . 22 3.2.3 Descriptor-based . 23 3.2.4 Similarity measure . 25 3.2.5 Network-based . 27 3.2.6 QSAR modeling to predict drug-drug interactions between ac- etaminophen and ingredients in energy drinks . 28 Chapter 4: Materials and Methods 29 4.1 QSAR modeling . 31 4.1.1 Data collection . 31 4.1.2 Preprocessing . 32 4.1.3 Classification . 33 4.1.4 Model evaluation . 34 4.2 In vitro model validation . 35 4.2.1 Materials . 35 4.2.2 Cell culture . 36 4.2.3 Transfection . 36 4.2.4 Exposure . 37 4.2.5 Cytotoxicity . 38 4.2.6 Data analysis . 38 Chapter 5: Results 40 5.1 Model evaluation . 40 5.1.1 SVM . 40 5.1.2 K-means and DDI network . 52 5.2 In vitro model validation . 59 5.2.1 Transfection . 59 5.2.2 Cytotoxicity . 59 vi Chapter 6: Conclusions and Future Work 61 6.1 Summary and discussion . 61 6.1.1 SVM . 61 6.1.2 K-means and DDI network . 63 6.1.3 In vitro model validation . 63 6.2 Future work . 64 6.2.1 Validating predictions . 64 6.2.2 Additional information for network analysis . 64 6.2.3 Indirect interactions . 64 6.3 Conclusion . 65 Bibliography 66 Appendix A: Additional Information 75 A.1 Complete list of QSAR descriptors . 75 vii List of Tables 5.1 Interactions predicted by QSAR model . 43 5.2 Known interactions for acetaminophen and select ingredients in energy drinks . 44 5.3 Overview of top predicted interactions for acetaminophen . 46 5.4 Overview of top predicted interactions for pyridoxine . 47 5.5 Overview of top predicted interactions for creatine . 48 5.6 Overview of top predicted interactions for L-carnitine . 49 5.7 Overview of top predicted interactions for taurine . 50 5.8 Overview of top predicted interactions for caffeine . 51 5.9 Cluster information for k=2 . 52 5.10 Cluster scores for k=2 . 52 5.11 Cluster information for k=3 . 53 5.12 Cluster scores for k=3 . 53 5.13 Cluster information for k=5 . 53 5.14 Cluster scores for k=5 . 54 5.15 Cluster information for k=7 . 54 5.16 Cluster scores for k=7 . 55 5.17 Cluster information for k=10 . 55 5.18 Cluster scores for k=10 . 56 viii 5.19 P450 content of clusters for k=2 . 58 5.20 LDH assay results . 60 ix List of Figures 2.1 Example of a multi-label data set . 10 2.2 Receptor agonist . 13 2.3 Receptor antagonist . 14 2.4 Signal transduction . 15 2.5 P450 metabolism of acetaminophen in the liver . 18 4.1 Model design process . 30 4.2 Drug interactions matrix . 32 5.1 Confusion matrix for QSAR model . 41 5.2 Summary of confusion matrices . 42 5.3 DDI network . 57 6.1 Indirect interactions . 65 x List of Abbreviations β-NADH β-Nicotinamide Adenine Dinucleotide ADMET Absorption, distribution, metabolism, excretion and transport ADR Adverse Drug Reaction AERS Adverse Event Reporting System ANOVA Analysis of Variance ATCC American Type Culture Collection BSA Bovine Serum Albumin CDK Chemistry Development Kit DDI Drug-Drug Interaction DMEM Dulbecco's Modified Eagle's Serum DT Decision Trees FBS Fetal Bovine Serum xi FDA Food and Drug Administration IPF Interaction Profile Fingerprint kNN Kappa Nearest Neighbour LDH Lactate Dehydrogenase NAPQI N-acetyl-p-benzoquinone-imine NCE New Chemical Entity OvR One-Vs.Rest P450 Cytochrome P450 PB Phosphate Buffer PBS Phosphate Buffered Saline PCA Principal Components Analysis QSAR Quantitative Structure-Activity Relationship RBF Radial Basis Function RF Random Forest SDS-PAGE Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis SRS Spontaneous Reporting System xii SVM Support Vector Machines TBS-T Tris-Buffered Saline and Tween 20 WEKA Waikato Environment for Knowledge Analysis xiii 1 Chapter 1 Introduction 1.1 Motivation Bringing a new therapeutic drug to market is expensive and time consuming. In the United States, bringing a single drug to market typically takes 10-15 years, and its cost can range from $800 million to $1 billion. For every one drug approved, there have been up to 10,000 drugs rejected and even after a drug has made it to market, there is still a potential for it to be removed [40]. Every year over 2 million people are affected by an adverse drug reaction (ADR) [24], out of which 26% can be attributed to drug-drug interactions (DDI) [29]. Elderly and cancer patient groups specifically, are at a higher risk from DDIs because they are administered multiple medications. Identifying DDIs early in the drug discovery and development process can reduce the number of hospitalizations due to drug in- teractions and allow more beneficial drugs to reach the market more quickly and at a lower cost. 1.2. PROBLEM 2 1.2 Problem Unfortunately, DDIs are difficult to test for due to their complex nature. Drugs are known to interact through a diverse array of mechanisms with the additional complication of affecting several different organs, tissues and pathways. This not only makes testing for all possible interactions difficult, but the prediction of DDIs is also often generalised to one mechanism of interaction, further limiting the discovery of new interactions. Current computational methods in predicting DDIs are aimed at expediting and enhancing the drug discovery and development process and offer several approaches. Various computational approaches have shown to be successful, but similar to in vitro and in vivo methods are often limited to only one of the many possible ways that drugs can interact. 1.3 Hypothesis It was hypothesized that computing could be used to build a classification model that predicts DDIs by analyzing and comparing the structure of drugs. Specifically, using a data set of molecular descriptors, computer modeling may be used to identify structural properties of drugs that lead to DDIs. Furthermore, it was hypothesized that pairs of drugs that were predicted to interact would lead to a change in cell death in vitro. 1.4. OBJECTIVES 3 1.4 Objectives A model based on the principle that structure determines function may present a solu- tion that encompasses multiple drug interaction mechanisms by stripping underlying DDIs to the molecular level.
Load more

Recommended publications
  • Medical Prescription Market Definitions
    Horvath Health Policy Innovations in Healthcare Financing Policy PO Box 196, College Park, MD 20741 202/465-5836 [email protected] Medical Prescription (Rx) Market Definitions Drug Product Terminology Small Molecule Drugs: Drugs with an active chemical ingredient that is not live, but chemically synthesized and typically are taken orally or topically, such as capsules, tablets, powders, ointments, and sprays. Brands: Also referred to “small molecule drugs,” brand drugs require original research and development for FDA licensure (also called “FDA approval”). They are approved (or licensed) under a New Drug Application (NDA). They are patent protected for 20 years total (which usually includes years clinical research before the drug is approved, or licensed). They are still referred to as brands even after the patent has expired (which distinguishes these drugs from generics). A brand can be ‘first in class” if it is a new chemical entity or new mechanism of action. It is a “me too drug” if it is not first in class. The definition of “me too” varies – different chemical composition, different mechanism of action. The drug is quite similar but different enough to get a patent. Generic: Small molecule products that are demonstrated to be clinically equivalent to a branded product (e.g., same active ingredient and route of administration, same mechanism of action). Generics do not require original research for FDA approval. Generics come to market only after the patent has expired on the brand product. Licensed under an Abbreviated New Drug Application (ANDA) by the FDA. Large Molecule Drugs: Commonly referred to as “biologics” and “biosimilars.” They contain live active ingredients and are generally infused or injected, and otherwise not taken orally or topically.
    [Show full text]
  • IND Exemptions Chart
    IND EXEMPTIONS Involving Drugs or Biologics Exemption 1 Exemption 4 Drug/Biologic US product Placebo * Drug/biologic product lawfully marketed in US * The clinical investigation involves the use of a placebo and the investigation does not otherwise * The investigation is not intended to be reported to FDA require submission of an IND as a well controlled study in support of a new indication for use nor intended to be used to support any other significant change in the labeling for the drug/biologic. * The investigation proposed is not intended to support a significant change in the advertising for the drug/ Exemption 5 biologic In Vivo Bioavailability or Bioequivalence * The investigation does not involve a route of * Test product does not contain a new chemical administration, dose, patient population, or other factor entity”’ as defined in 21 CFR 314.108(a) [** a drug that significantly increases the risks (or decreases the that contains no active moiety that has been acceptability of the risks) associated with the use of the approved by FDA in any other application. drug/biologic * The study does not involve a radioactively labeled * The investigation will be conducted in compliance with drug product. FDA regulations for the Protection of Human Subjects and Institutional Review Boards 21 CFR 50 & 56 * The study does not involve a cytotoxic drug product. * The investigation will be conducted in compliance with The investigator will conduct a bioavailability or the FDA requirements for Promotion and Charging for bioequivalence study
    [Show full text]
  • United States District Court for the District of Columbia
    Case 1:15-cv-00802-RC Document 29 Filed 03/15/16 Page 1 of 33 UNITED STATES DISTRICT COURT FOR THE DISTRICT OF COLUMBIA FERRING PHARMACEUTICALS, INC., : : Plaintiff, : Civil Action No.: 15-0802 (RC) : v. : Re Document Nos.: 20, 22 : SYLVIA M. BURWELL, et al., : : Defendants. : MEMORANDUM OPINION GRANTING IN PART AND DENYING IN PART DEFENDANTS’ MOTION FOR SUMMARY JUDGMENT AND DENYING PLAINTIFF’S MOTION FOR SUMMARY JUDGMENT I. INTRODUCTION Plaintiff Ferring Pharmaceuticals, Inc. (“Ferring”) is the manufacturer of PREPOPIK, a fixed-dose combination drug product that contains three drug substances: sodium picosulfate, magnesium oxide, and anhydrous citric acid. When it submitted a New Drug Application (“NDA”) for PREPOPIK to the U.S. Food and Drug Administration (“the FDA”), Ferring sought a five-year period of marketing exclusivity because one of the drug substances, sodium picosulfate, had never previously been approved in a NDA. The Federal Food, Drug, and Cosmetics Act (“FDCA”) provides for a five-year period of marketing exclusivity when a drug application is approved “for a drug, no active ingredient (including any ester or salt of the active ingredient) of which has been approved in any other application.” 21 U.S.C. § 355(j)(5)(F)(ii). During that five-year period, “no application may be submitted . which refers to the drug for which the subsection (b) application was submitted.” Id. The dispute in this case is whether the statutory term “drug,” as used in this provision of the FDCA, can reasonably be read to refer to a “drug product” (the finished dosage form of a Case 1:15-cv-00802-RC Document 29 Filed 03/15/16 Page 2 of 33 drug), or must be read to refer to a “drug substance” (the active ingredient of the drug).
    [Show full text]
  • New Chemical Entity Exclusivity Determinations for Certain Fixed- Combination Drug Products
    New Chemical Entity Exclusivity Determinations for Certain Fixed- Combination Drug Products Guidance for Industry U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) October 2014 Procedural New Chemical Entity Exclusivity Determinations for Certain Fixed- Combination Drug Products Guidance for Industry Office of Communications, Division of Drug Information Center for Drug Evaluation and Research Food and Drug Administration 10001 New Hampshire Ave., Hillandale Bldg., 4th Floor Silver Spring, MD 20993 Phone: 855-543-3784 or 301-796-3400; Fax: 301-431-6353 [email protected] http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) October 2014 Procedural Contains Nonbinding Recommendations TABLE OF CONTENTS I. INTRODUCTION............................................................................................................. 1 II. BACKGROUND ............................................................................................................... 2 III. STATUTORY AND REGULATORY FRAMEWORK ............................................... 2 IV. FDA’S HISTORICAL INTERPRETATION OF THE 5-YEAR NCE EXCLUSIVITY PROVISIONS ....................................................................................... 5 V. REVISED AGENCY INTERPRETATION OF THE 5-YEAR NCE EXCLUSIVITY PROVISIONS ...................................................................................................................
    [Show full text]
  • ONLINE SUPPLEMENTARY TABLE Table 2. Differentially Expressed
    ONLINE SUPPLEMENTARY TABLE Table 2. Differentially Expressed Probe Sets in Livers of GK Rats. A. Immune/Inflammatory (67 probe sets, 63 genes) Age Strain Probe ID Gene Name Symbol Accession Gene Function 5 WKY 1398390_at small inducible cytokine B13 precursor Cxcl13 AA892854 chemokine activity; lymph node development 5 WKY 1389581_at interleukin 33 Il33 BF390510 cytokine activity 5 WKY *1373970_at interleukin 33 Il33 AI716248 cytokine activity 5 WKY 1369171_at macrophage stimulating 1 (hepatocyte growth factor-like) Mst1; E2F2 NM_024352 serine-throenine kinase; tumor suppression 5 WKY 1388071_x_at major histocompatability antigen Mhc M24024 antigen processing and presentation 5 WKY 1385465_at sialic acid binding Ig-like lectin 5 Siglec5 BG379188 sialic acid-recognizing receptor 5 WKY 1393108_at major histocompatability antigen Mhc BM387813 antigen processing and presentation 5 WKY 1388202_at major histocompatability antigen Mhc BI395698 antigen processing and presentation 5 WKY 1371171_at major histocompatability antigen Mhc M10094 antigen processing and presentation 5 WKY 1370382_at major histocompatability antigen Mhc BI279526 antigen processing and presentation 5 WKY 1371033_at major histocompatability antigen Mhc AI715202 antigen processing and presentation 5 WKY 1383991_at leucine rich repeat containing 8 family, member E Lrrc8e BE096426 proliferation and activation of lymphocytes and monocytes. 5 WKY 1383046_at complement component factor H Cfh; Fh AA957258 regulation of complement cascade 4 WKY 1369522_a_at CD244 natural killer
    [Show full text]
  • Natural Products As Leads to Potential Drugs: an Old Process Or the New Hope for Drug Discovery?
    J. Med. Chem. 2008, 51, 2589–2599 2589 Natural Products as Leads to Potential Drugs: An Old Process or the New Hope for Drug Discovery? David J. Newman† Natural Products Branch, DeVelopmental Therapeutics Program, DCTD, National Cancer InstitutesFrederick, P.O. Box B, Frederick, Maryland 21702 ReceiVed April 5, 2007 I. Introduction From approximately the early 1980s, the “influence of natural products” upon drug discovery in all therapeutic areas apparently has been on the wane because of the advent of combinatorial chemistry technology and the “associated expectation” that these techniques would be the future source of massive numbers of novel skeletons and drug leads/new chemical entities (NCEa) where the intellectual property aspects would be very simple. As a result, natural product work in the pharmaceutical industry, except for less than a handful of large pharmaceutical compa- nies, effectively ceased from the end of the 1980s. Figure 1. Source of small molecule drugs, 1981–2006: major What has now transpired (cf. evidence shown in Newman categories, N ) 983 (in percentages). Codes are as in ref 1. Major and Cragg, 20071 and Figures 1 and 2 below showing the categories are as follows: “N”, natural product; “ND”, derived from a natural product and usually a semisynthetic modification; “S”, totally continued influence of natural products as leads to or sources synthetic drug often found by random screening/modification of an of drugs over the past 26 years (1981–2006)) is that, to date, existing agent; “S*”, made by total synthesis, but the pharmacophore there has only been one de novo combinatorial NCE approved is/was from a natural product.
    [Show full text]
  • Relating Metatranscriptomic Profiles to the Micropollutant
    1 Relating Metatranscriptomic Profiles to the 2 Micropollutant Biotransformation Potential of 3 Complex Microbial Communities 4 5 Supporting Information 6 7 Stefan Achermann,1,2 Cresten B. Mansfeldt,1 Marcel Müller,1,3 David R. Johnson,1 Kathrin 8 Fenner*,1,2,4 9 1Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, 10 Switzerland. 2Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 11 Zürich, Switzerland. 3Institute of Atmospheric and Climate Science, ETH Zürich, 8092 12 Zürich, Switzerland. 4Department of Chemistry, University of Zürich, 8057 Zürich, 13 Switzerland. 14 *Corresponding author (email: [email protected] ) 15 S.A and C.B.M contributed equally to this work. 16 17 18 19 20 21 This supporting information (SI) is organized in 4 sections (S1-S4) with a total of 10 pages and 22 comprises 7 figures (Figure S1-S7) and 4 tables (Table S1-S4). 23 24 25 S1 26 S1 Data normalization 27 28 29 30 Figure S1. Relative fractions of gene transcripts originating from eukaryotes and bacteria. 31 32 33 Table S1. Relative standard deviation (RSD) for commonly used reference genes across all 34 samples (n=12). EC number mean fraction bacteria (%) RSD (%) RSD bacteria (%) RSD eukaryotes (%) 2.7.7.6 (RNAP) 80 16 6 nda 5.99.1.2 (DNA topoisomerase) 90 11 9 nda 5.99.1.3 (DNA gyrase) 92 16 10 nda 1.2.1.12 (GAPDH) 37 39 6 32 35 and indicates not determined. 36 37 38 39 S2 40 S2 Nitrile hydration 41 42 43 44 Figure S2: Pearson correlation coefficients r for rate constants of bromoxynil and acetamiprid with 45 gene transcripts of ECs describing nucleophilic reactions of water with nitriles.
    [Show full text]
  • 1097.Full.Pdf
    1521-009X/47/10/1097–1099$35.00 https://doi.org/10.1124/dmd.119.088708 DRUG METABOLISM AND DISPOSITION Drug Metab Dispos 47:1097–1099, October 2019 Copyright ª 2019 by The American Society for Pharmacology and Experimental Therapeutics Special Section on Pharmacokinetic and Drug Metabolism Properties of Novel Therapeutic Modalities—Commentary Pharmacokinetic and Drug Metabolism Properties of Novel Therapeutic Modalities Brooke M. Rock and Robert S. Foti Pharmacokinetics and Drug Metabolism, Amgen Research, South San Francisco, California (B.M.R.) and Pharmacokinetics and Drug Metabolism, Amgen Research, Cambridge, Massachusetts (R.S.F.) Received July 10, 2019; accepted July 26, 2019 Downloaded from ABSTRACT The discovery and development of novel pharmaceutical therapies in experimental and analytical tools will become increasingly is rapidly transitioning from a small molecule–dominated focus to evident, both to increase the speed and efficiency of identifying safe a more balanced portfolio consisting of small molecules, mono- and efficacious molecules and simultaneously decreasing our de- clonal antibodies, engineered proteins (modified endogenous pro- pendence on in vivo studies in preclinical species. The research and dmd.aspetjournals.org teins, bispecific antibodies, and fusion proteins), oligonucleotides, commentary included in this special issue will provide researchers, and gene-based therapies. This commentary, and the special issue clinicians, and the patients we serve more options in the ongoing as a whole, aims to highlight
    [Show full text]
  • Generic Drug Development and Safety Evaluation
    U.S. FOOD & DRUG ADMIN I STRATION Generic Drug Development and Safety Evaluation Howard D. Chazin, MD, MBA, Director CDER Office of Generic Drugs Clinical Safety Surveillance Staff (CSSS) CDER Pediatric Advisory Committee Meeting September 20, 2018 1 Outline 1. Basis for Generic Drug Approvals 2. Contents of an Abbreviated New Drug Application (ANDA) 3. Generic Drug Development • Pharmaceutical Equivalence • Bioequivalence • Therapeutic Equivalence 4. Generic Drug Safety Surveillance • Premarket • Postmarketing 2 Generic Drug Approval • Approval of generic drug starts with a “listed drug” – generally an “innovator” or “brand name” drug. • This is the reference listed drug (RLD) • Abbreviated New Drug Application (ANDA) relies on FDA’s finding of safety and effectiveness for the RLD during the Investigational New Drug (IND) and New Drug Application (NDA) phases of drug review. • Requires demonstration of “sameness” of a number of characteristics + additional information to permit reliance on the RLD 3 Modern Generic Drug Approval Pathway Drug Price Competition and Patent Term Restoration Act of 1984 (Hatch-Waxman Amendments) • First statutory provisions expressly pertaining to generic drugs. • Created the basic scheme under which generic drugs are approved today. • Allowed FDA to approve - under new section 505(j) - generic applications for duplicates of drugs submitted under 505(b). 4 Hatch-Waxman Amendments • Brand Industry Gains: • 5-year New Chemical Entity (NCE) Exclusivity • 3-year New Clinical Studies Exclusivity • Patent Term Extension
    [Show full text]
  • Pharmacophore Hybridisation and Nanoscale Assembly to Discover Self-Delivering Lysosomotropic New-Chemical Entities for Cancer Therapy
    ARTICLE https://doi.org/10.1038/s41467-020-18399-4 OPEN Pharmacophore hybridisation and nanoscale assembly to discover self-delivering lysosomotropic new-chemical entities for cancer therapy Zhao Ma 1,2, Jin Li1, Kai Lin 1, Mythili Ramachandran1, Dalin Zhang1, Megan Showalter3, Cristabelle De Souza1, Aaron Lindstrom1, Lucas N. Solano1, Bei Jia1, Shiro Urayama 4, Yuyou Duan5, ✉ Oliver Fiehn 3, Tzu-yin Lin6, Minyong Li 2,7 & Yuanpei Li 1 1234567890():,; Integration of the unique advantages of the fields of drug discovery and drug delivery is invaluable for the advancement of drug development. Here we propose a self-delivering one-component new-chemical-entity nanomedicine (ONN) strategy to improve cancer therapy through incorporation of the self-assembly principle into drug design. A lysosomo- tropic detergent (MSDH) and an autophagy inhibitor (Lys05) are hybridised to develop bisaminoquinoline derivatives that can intrinsically form nanoassemblies. The selected BAQ12 and BAQ13 ONNs are highly effective in inducing lysosomal disruption, lysosomal dysfunction and autophagy blockade and exhibit 30-fold higher antiproliferative activity than hydroxychloroquine used in clinical trials. These single-drug nanoparticles demonstrate excellent pharmacokinetic and toxicological profiles and dramatic antitumour efficacy in vivo. In addition, they are able to encapsulate and deliver additional drugs to tumour sites and are thus promising agents for autophagy inhibition-based combination therapy. Given their transdisciplinary advantages, these BAQ ONNs have enormous potential to improve cancer therapy. 1 Department of Biochemistry and Molecular Medicine, UC Davis Comprehensive Cancer Center, University of California Davis, Sacramento, CA 95817, USA. 2 Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (MOE), School of Pharmacy, Cheeloo College of Medicine, Shandong University, Jinan 250012 Shandong, China.
    [Show full text]
  • The Biosynthesis Reaction of Hypotaurine to Taurine
    UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Jackson College of Graduate Studies The Biosynthesis Reaction of Hypotaurine to Taurine A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Roxanna Q. Grove Edmond, Oklahoma 2018 Acknowledgments Working on this project has been a period of intense learning for me, not only in the scientific arena, but also on a personal level. Writing this thesis has had a significant impact on me. I would like to reflect on people who have been supported and helped me so much throughout this period. First of all, I would like to express my gratitude toward my advisor, Dr. Steven J. Karpowicz, for his devotion, inspiration, and guidance. I am so grateful to have the opportunity to work with such an intelligent, dedicated, and patient professor. I appreciate his vast knowledge and skills in many areas such as biochemistry, genetics, and bioinformatics, and his assistance in writing this thesis. I would like to thank the other members of my committee, Dr. Nikki Seagraves, Dr. Hari Kotturi, and Dr. Lilian Chooback, for their guidance, support, and for providing materials throughout this project. An exceptional thanks go to Dr. John Bowen of the Department of Chemistry for advice in the analytical laboratory and Dr. Susan L. Nimmo from the Department of Chemistry and Biochemistry at the University of Oklahoma for assistance with NMR. This project was supported by funding from the College of Mathematics and Science and a Research, Creative, and Scholarly Activities (RCSA) grant from the Office of High Impact Practices at UCO.
    [Show full text]
  • 2018 Annual Meeting Proceedings
    Downloaded from orbit.dtu.dk on: Oct 04, 2021 Genomics driven discovery and engineering of fungal polycyclic polyketides Subko, Karolina; Wolff, Peter B.; Theobald, Sebastian; Frisvad, Jens C.; Gotfredsen, Charlotte H.; Andersen, Mikael R.; Mortensen, Uffe H.; Larsen, Thomas O. Publication date: 2018 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Subko, K., Wolff, P. B., Theobald, S., Frisvad, J. C., Gotfredsen, C. H., Andersen, M. R., Mortensen, U. H., & Larsen, T. O. (2018). Genomics driven discovery and engineering of fungal polycyclic polyketides. 68. Abstract from The American Society of Pharmacognosy Annual Meeting 2018, Lexington, Kentucky, United States. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstracts | 2018 Annual Meeting of the ASP | July 21-25, 2018 | Lexington, Kentucky, USA 2 The American Society of Pharmacognosy Annual Meeting July 21-25, 2018 Hilton Lexington Downtown Lexington, Kentucky ENGAGE IN THE CONVO #ASP2018 Welcome to 59th Annual Meeting of the American Society of Pharmacognosy (ASP) 2018 in beautiful downtown Lexington, Kentucky! The Local Organizing Committee would like to thank you for joining us for the ASP meeting at the Hilton Lexington Downtown.
    [Show full text]