The Pharmaceuticals and Medical Devices Agency Annual Report Fy 2010 Table of Contents
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Table S1: Sensitivity, Specificity, PPV, NPV, and F1 Score of NLP Vs. ICD for Identification of Symptoms for (A) Biome Developm
Table S1: Sensitivity, specificity, PPV, NPV, and F1 score of NLP vs. ICD for identification of symptoms for (A) BioMe development cohort; (B) BioMe validation cohort; (C) MIMIC-III; (D) 1 year of notes from patients in BioMe calculated using manual chart review. A) Fatigue Nausea and/or vomiting Anxiety Depression NLP (95% ICD (95% CI) P NLP (95% CI) ICD (95% CI) P NLP (95% CI) ICD (95% CI) P NLP (95% CI) ICD (95% CI) P CI) 0.99 (0.93- 0.59 (0.43- <0.00 0.25 (0.12- <0.00 <0.00 0.54 (0.33- Sensitivity 0.99 (0.9 – 1) 0.98 (0.88 -1) 0.3 (0.15-0.5) 0.85 (0.65-96) 0.02 1) 0.73) 1 0.42) 1 1 0.73) 0.57 (0.29- 0.9 (0.68- Specificity 0.89 (0.4-1) 0.75 (0.19-1) 0.68 0.97 (0.77-1) 0.03 0.98 (0.83-1) 0.22 0.81 (0.53-0.9) 0.96 (0.79-1) 0.06 0.82) 0.99) 0.99 (0.92- 0.86 (0.71- 0.94 (0.79- 0.79 (0.59- PPV 0.96 (0.82-1) 0.3 0.95 (0.66-1) 0.02 0.95 (0.66-1) 0.16 0.93 (0.68-1) 0.12 1) 0.95) 0.99) 0.92) 0.13 (0.03- <0.00 0.49 (0.33- <0.00 0.66 (0.48- NPV 0.89 (0.4-1) 0.007 0.94 (0.63-1) 0.34 (0.2-0.51) 0.97 (0.81-1) 0.86 (0.6-0.95) 0.04 0.35) 1 0.65) 1 0.81) <0.00 <0.00 <0.00 F1 Score 0.99 0.83 0.88 0.57 0.95 0.63 0.82 0.79 0.002 1 1 1 Itching Cramp Pain NLP (95% ICD (95% CI) P NLP (95% CI) ICD (95% CI) P NLP (95% CI) ICD (95% CI) P CI) 0.98 (0.86- 0.24 (0.09- <0.00 0.09 (0.01- <0.00 0.52 (0.37- <0.00 Sensitivity 0.98 (0.85-1) 0.99 (0.93-1) 1) 0.45) 1 0.29) 1 0.66) 1 0.89 (0.72- 0.5 (0.37- Specificity 0.96 (0.8-1) 0.98 (0.86-1) 0.68 0.98 (0.88-1) 0.18 0.5 (0-1) 1 0.98) 0.66) 0.88 (0.69- PPV 0.96 (0.8-1) 0.8 (0.54-1) 0.32 0.8 (0.16-1) 0.22 0.99 (0.93-1) 0.98 (0.87-1) NA* 0.97) 0.98 (0.85- 0.57 (0.41- <0.00 0.58 (0.43- <0.00 NPV 0.98 (0.86-1) 0.5 (0-1) 0.02 (0-0.08) NA* 1) 0.72) 1 0.72) 1 <0.00 <0.00 <0.00 F1 Score 0.97 0.56 0.91 0.28 0.99 0.68 1 1 1 *Denotes 95% confidence intervals and P values that could not be calculated due to insufficient cells in 2x2 tables. -
Product List March 2019 - Page 1 of 53
Wessex has been sourcing and supplying active substances to medicine manufacturers since its incorporation in 1994. We supply from known, trusted partners working to full cGMP and with full regulatory support. Please contact us for details of the following products. Product CAS No. ( R)-2-Methyl-CBS-oxazaborolidine 112022-83-0 (-) (1R) Menthyl Chloroformate 14602-86-9 (+)-Sotalol Hydrochloride 959-24-0 (2R)-2-[(4-Ethyl-2, 3-dioxopiperazinyl) carbonylamino]-2-phenylacetic 63422-71-9 acid (2R)-2-[(4-Ethyl-2-3-dioxopiperazinyl) carbonylamino]-2-(4- 62893-24-7 hydroxyphenyl) acetic acid (r)-(+)-α-Lipoic Acid 1200-22-2 (S)-1-(2-Chloroacetyl) pyrrolidine-2-carbonitrile 207557-35-5 1,1'-Carbonyl diimidazole 530-62-1 1,3-Cyclohexanedione 504-02-9 1-[2-amino-1-(4-methoxyphenyl) ethyl] cyclohexanol acetate 839705-03-2 1-[2-Amino-1-(4-methoxyphenyl) ethyl] cyclohexanol Hydrochloride 130198-05-9 1-[Cyano-(4-methoxyphenyl) methyl] cyclohexanol 93413-76-4 1-Chloroethyl-4-nitrophenyl carbonate 101623-69-2 2-(2-Aminothiazol-4-yl) acetic acid Hydrochloride 66659-20-9 2-(4-Nitrophenyl)ethanamine Hydrochloride 29968-78-3 2,4 Dichlorobenzyl Alcohol (2,4 DCBA) 1777-82-8 2,6-Dichlorophenol 87-65-0 2.6 Diamino Pyridine 136-40-3 2-Aminoheptane Sulfate 6411-75-2 2-Ethylhexanoyl Chloride 760-67-8 2-Ethylhexyl Chloroformate 24468-13-1 2-Isopropyl-4-(N-methylaminomethyl) thiazole Hydrochloride 908591-25-3 4,4,4-Trifluoro-1-(4-methylphenyl)-1,3-butane dione 720-94-5 4,5,6,7-Tetrahydrothieno[3,2,c] pyridine Hydrochloride 28783-41-7 4-Chloro-N-methyl-piperidine 5570-77-4 -
Specifications of Approved Drug Compound Library
Annexure-I : Specifications of Approved drug compound library The compounds should be structurally diverse, medicinally active, and cell permeable Compounds should have rich documentation with structure, Target, Activity and IC50 should be known Compounds which are supplied should have been validated by NMR and HPLC to ensure high purity Each compound should be supplied as 10mM solution in DMSO and at least 100µl of each compound should be supplied. Compounds should be supplied in screw capped vial arranged as 96 well plate format. -
Identification of Compounds That Rescue Otic and Myelination
RESEARCH ARTICLE Identification of compounds that rescue otic and myelination defects in the zebrafish adgrg6 (gpr126) mutant Elvira Diamantopoulou1†, Sarah Baxendale1†, Antonio de la Vega de Leo´ n2, Anzar Asad1, Celia J Holdsworth1, Leila Abbas1, Valerie J Gillet2, Giselle R Wiggin3, Tanya T Whitfield1* 1Bateson Centre and Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom; 2Information School, University of Sheffield, Sheffield, United Kingdom; 3Sosei Heptares, Cambridge, United Kingdom Abstract Adgrg6 (Gpr126) is an adhesion class G protein-coupled receptor with a conserved role in myelination of the peripheral nervous system. In the zebrafish, mutation of adgrg6 also results in defects in the inner ear: otic tissue fails to down-regulate versican gene expression and morphogenesis is disrupted. We have designed a whole-animal screen that tests for rescue of both up- and down-regulated gene expression in mutant embryos, together with analysis of weak and strong alleles. From a screen of 3120 structurally diverse compounds, we have identified 68 that reduce versican b expression in the adgrg6 mutant ear, 41 of which also restore myelin basic protein gene expression in Schwann cells of mutant embryos. Nineteen compounds unable to rescue a strong adgrg6 allele provide candidates for molecules that may interact directly with the Adgrg6 receptor. Our pipeline provides a powerful approach for identifying compounds that modulate GPCR activity, with potential impact for future drug design. DOI: https://doi.org/10.7554/eLife.44889.001 *For correspondence: [email protected] †These authors contributed Introduction equally to this work Adgrg6 (Gpr126) is an adhesion (B2) class G protein-coupled receptor (aGPCR) with conserved roles in myelination of the vertebrate peripheral nervous system (PNS) (reviewed in Langenhan et al., Competing interest: See 2016; Patra et al., 2014). -
A Framework for Assurance of Medication Safety Using Machine Learning
A Framework for Assurance of Medication Safety using Machine Learning Yan Jia1 Tom Lawton2 John McDermid1 Eric Rojas3 Ibrahim Habli1 Abstract— Medication errors continue to be the leading cause of avoidable patient harm in hospitals. This paper sets out a framework to assure medication safety that combines machine learning and safety engineering methods. It uses safety analysis to proactively identify potential causes of medication error, based on expert opinion. As healthcare is now data rich, it is possible to augment safety analysis with machine learning to discover actual causes of medication error from the data, and to identify where they deviate from what was predicted in the safety analysis. Combining these two views has the potential to enable the risk of medication errors to be managed proactively and dynamically. We apply the framework to a case study involving thoracic surgery, e.g. oesophagectomy, where errors in giving beta-blockers can be critical to control atrial fibrillation. This case study combines a HAZOP-based safety analysis method known as SHARD with Bayesian network structure learning and process mining to produce the analysis results, showing the potential of the framework for ensuring patient safety, and for transforming the way that safety is managed in complex healthcare environments. Keywords—medication safety, machine learning, proactive safety management 1. INTRODUCTION Safety analysis is both predictive and reactive. It aims to identify hazards, hazard causes and mitigations, and associated risks before a system is deployed. The system is then monitored during its deployment to manage risks. When systems are used in highly controlled environments, built using components with a long service history, these predictions can be accurate. -
Medication Instructions for Allergy Patients
MEDICATION INSTRUCTIONS FOR ALLERGY PATIENTS Drugs which contain antihistamine or have antihistaminic effects can result in negative reactions to skin testing. As a result, it may not be possible to properly interpret skin test results, and testing may have to be repeated at a later date. While this list is extensive, it is NOT all inclusive (particularly of the various brand names). Discontinue ALL antihistamines including the following medications seven (7) days prior to skin testing (unless longer time specified): Antihistamines – Generic name (Brand name(s)): Cetirizine (Zyrtec, Zyrtec-D) Hydroxyzine (Vistaril, Atarax) Desloratadine (Clarinex) Levocetirizine (Xyzal) Fexofenadine (Allegra, Allegra-D) Loratadine (Claritin, Claritin-D, Alavert) Diphenhydramine (Aleve PM, Benadryl, Bayer P.M., Benylin, Contac P.M., Doans P.M, Excedrin PM, Legatrin P.M.. Nytol, Tylenol Nighttime, Unisom, Zzzquil) Chlorpheniramine (Aller-Chlor, Allerest, Alka Seltzer Plus, Chlor-Trimeton, Comtrex, Contac, Co-Pyronil, Coricidin, CTM, Deconamine, Dristan, Dura-tap, Naldecon, Ornade Spansules, Rondec, Sinutab, Teldrin, Triaminic, Triaminicin, Tylenol Allergy) Azatadine (Optimine, Trinalin) Doxylamine (Nyquil) Brompheniramine (Bromfed, Dimetane, Dimetapp) Meclizine (Antivert) Carbinoxamine (Clistin, Rondec) Pheniramine Clemastine (Tavist) Phenyltoloxamine (Nadecon) Cyclizine (Marezine) Promethazine (Phenergan) Cyprohepatidine (Periactin) (9 days) Pyrilamine (Mepyramine) Dexbrompheniramine (Drixoral) Quinacrine (Atabrine) Dexchlorpheniramine (Extendryl, Polaramine) -
A Comparative Effectiveness Meta-Analysis of Drugs for the Prophylaxis of Migraine Headache
University of South Florida Masthead Logo Scholar Commons School of Information Faculty Publications School of Information 7-2015 A Comparative Effectiveness Meta-Analysis of Drugs for the Prophylaxis of Migraine Headache Authors: Jeffrey L. Jackson, Elizabeth Cogbill, Rafael Santana-Davila, Christina Eldredge, William Collier, Andrew Gradall, Neha Sehgal, and Jessica Kuester OBJECTIVE: To compare the effectiveness and side effects of migraine prophylactic medications. DESIGN: We performed a network meta-analysis. Data were extracted independently in duplicate and quality was assessed using both the JADAD and Cochrane Risk of Bias instruments. Data were pooled and network meta-analysis performed using random effects models. DATA SOURCES: PUBMED, EMBASE, Cochrane Trial Registry, bibliography of retrieved articles through 18 May 2014. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included randomized controlled trials of adults with migraine headaches of at least 4 weeks in duration. RESULTS: Placebo controlled trials included alpha blockers (n = 9), angiotensin converting enzyme inhibitors (n = 3), angiotensin receptor blockers (n = 3), anticonvulsants (n = 32), beta-blockers (n = 39), calcium channel blockers (n = 12), flunarizine (n = 7), serotonin reuptake inhibitors (n = 6), serotonin norepinephrine reuptake inhibitors (n = 1) serotonin agonists (n = 9) and tricyclic antidepressants (n = 11). In addition there were 53 trials comparing different drugs. Drugs with at least 3 trials that were more effective than placebo for episodic migraines -
Annual Report 2016
Annexes to the annual report of the European Medicines Agency 2016 Annex 1 – Members of the Management Board ............................................................... 2 Annex 2 - Members of the Committee for Medicinal Products for Human Use ...................... 4 Annex 3 – Members of the Pharmacovigilance Risk Assessment Committee ........................ 6 Annex 4 – Members of the Committee for Medicinal Products for Veterinary Use ................. 8 Annex 5 – Members of the Committee on Orphan Medicinal Products .............................. 10 Annex 6 – Members of the Committee on Herbal Medicinal Products ................................ 12 Annex 7 – Committee for Advanced Therapies .............................................................. 14 Annex 8 – Members of the Paediatric Committee .......................................................... 16 Annex 9 – Working parties and working groups ............................................................ 18 Annex 10 – CHMP opinions: initial evaluations and extensions of therapeutic indication ..... 24 Annex 10a – Guidelines and concept papers adopted by CHMP in 2016 ............................ 25 Annex 11 – CVMP opinions in 2016 on medicinal products for veterinary use .................... 33 Annex 11a – 2016 CVMP opinions on extensions of indication for medicinal products for veterinary use .......................................................................................................... 39 Annex 11b – Guidelines and concept papers adopted by CVMP in 2016 ........................... -
Skim - a Generalized Literature-Based Discovery System For
bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.343012; this version posted October 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. SKiM - A generalized literature-based discovery system for uncovering novel biomedical knowledge from PubMed Kalpana Raja1, John Steill1, Ian Ross2, Lam C Tsoi3,4,5, Finn Kuusisto1, Zijian Ni6, Miron Livny2, James Thomson1,7 and Ron Stewart1* 1Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA 2Center for High Throughput Computing, Computer Sciences Department, University of Wisconsin, Madison, WI, USA 3Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA 4Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA 5Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA 6Department of Statistics, University of Wisconsin, Madison, WI, USA 7Department of Cell and Regenerative Biology, University of Wisconsin, Madison, WI, USA *Corresponding author Email: [email protected] Phone: (608) 316-4349 Home page: https://morgridge.org/profile/ron-stewart/ bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.343012; this version posted October 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Literature-based discovery (LBD) uncovers undiscovered public knowledge by linking terms A to C via a B intermediate. Existing LBD systems are limited to process certain A, B, and C terms, and many are not maintained. We present SKiM (Serial KinderMiner), a generalized LBD system for processing any combination of A, Bs, and Cs. -
WO 2013/061161 A2 2 May 2013 (02.05.2013) 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 2013/061161 A2 2 May 2013 (02.05.2013) P O P C T (51) International Patent Classification: (81) Designated States (unless otherwise indicated, for every A61K 31/337 (2006.01) A61K 31/48 (2006.01) kind of national protection available): AE, AG, AL, AM, A61K 31/395 (2006.01) A61K 31/51 (2006.01) AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, A61K 31/4174 (2006.01) A61K 31/549 (2006.01) BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, A61K 31/428 (2006.01) A61K 31/663 (2006.01) DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, (21) International Application Number: KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, PCT/IB20 12/002768 ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, (22) International Filing Date: NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, 25 October 2012 (25.10.2012) RW, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, (25) Filing Language: English ZM, ZW. (26) Publication Language: English (84) Designated States (unless otherwise indicated, for every (30) Priority Data: kind of regional protection available): ARIPO (BW, GH, 61/552,922 28 October 201 1 (28. -
(12) United States Patent (10) Patent No.: US 8,039,019 B2 Hahn (45) Date of Patent: Oct
USO08039019B2 (12) United States Patent (10) Patent No.: US 8,039,019 B2 Hahn (45) Date of Patent: Oct. 18, 2011 (54) PHARMACEUTICAL FORMULATION (56) References Cited CONTAINING A BIGUANDE AND AN ANGOTENSIN ANTAGONST U.S. PATENT DOCUMENTS 5,196,444 A 3, 1993 Naka et al. (75) Inventor: Elliot F. Hahn, North Miami Beach, FL 5,534,534 A 7/1996 Makino et al. (US) 5,650,170 A 7/1997 Wright et al. 5,705,517 A 1/1998 Naka et al. (73) Assignee: Andrx Labs, LLC, Davie, FL (US) 6,099,859 A 8/2000 Cheng et al. 6,099,862 A 8, 2000 Chen et al. 6,682,759 B2 1/2004 Lim et al. (*) Notice: Subject to any disclaimer, the term of this 7,588,779 B2 9, 2009 Hahn patent is extended or adjusted under 35 2001 OO24659 A1 9, 2001 Chen et al. U.S.C. 154(b) by 54 days. 2004.0034.065 A1 2/2004 Allison et al. 2004/0106660 A1 6/2004 Kositprapa et al. (21) Appl. No.: 12/535,833 2004O161462 A1 8/2004 Kositprapa et al. 2004/0219209 A1 11/2004 Chen et al. (22) Filed: Aug. 5, 2009 2005/0226928 A1 10, 2005 LOdin et al. FOREIGN PATENT DOCUMENTS Prior Publication Data (65) WO WO9947 125 9, 1999 US 2009/O2976OO A1 Dec. 3, 2009 WO WOO215933 2, 2002 Related U.S. Application Data OTHER PUBLICATIONS Graffeo, M., International Search Report, PCT/US05/18939, Mar. 9. (62) Division of application No. 1 1/628,066, filed as 2006. -
Ehealth DSI [Ehdsi V2.2.2-OR] Ehealth DSI – Master Value Set
MTC eHealth DSI [eHDSI v2.2.2-OR] eHealth DSI – Master Value Set Catalogue Responsible : eHDSI Solution Provider PublishDate : Wed Nov 08 16:16:10 CET 2017 © eHealth DSI eHDSI Solution Provider v2.2.2-OR Wed Nov 08 16:16:10 CET 2017 Page 1 of 490 MTC Table of Contents epSOSActiveIngredient 4 epSOSAdministrativeGender 148 epSOSAdverseEventType 149 epSOSAllergenNoDrugs 150 epSOSBloodGroup 155 epSOSBloodPressure 156 epSOSCodeNoMedication 157 epSOSCodeProb 158 epSOSConfidentiality 159 epSOSCountry 160 epSOSDisplayLabel 167 epSOSDocumentCode 170 epSOSDoseForm 171 epSOSHealthcareProfessionalRoles 184 epSOSIllnessesandDisorders 186 epSOSLanguage 448 epSOSMedicalDevices 458 epSOSNullFavor 461 epSOSPackage 462 © eHealth DSI eHDSI Solution Provider v2.2.2-OR Wed Nov 08 16:16:10 CET 2017 Page 2 of 490 MTC epSOSPersonalRelationship 464 epSOSPregnancyInformation 466 epSOSProcedures 467 epSOSReactionAllergy 470 epSOSResolutionOutcome 472 epSOSRoleClass 473 epSOSRouteofAdministration 474 epSOSSections 477 epSOSSeverity 478 epSOSSocialHistory 479 epSOSStatusCode 480 epSOSSubstitutionCode 481 epSOSTelecomAddress 482 epSOSTimingEvent 483 epSOSUnits 484 epSOSUnknownInformation 487 epSOSVaccine 488 © eHealth DSI eHDSI Solution Provider v2.2.2-OR Wed Nov 08 16:16:10 CET 2017 Page 3 of 490 MTC epSOSActiveIngredient epSOSActiveIngredient Value Set ID 1.3.6.1.4.1.12559.11.10.1.3.1.42.24 TRANSLATIONS Code System ID Code System Version Concept Code Description (FSN) 2.16.840.1.113883.6.73 2017-01 A ALIMENTARY TRACT AND METABOLISM 2.16.840.1.113883.6.73 2017-01