Sarcoma Metabolomics: Current Horizons and Future Perspectives
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WO 2018/223101 Al 06 December 2018 (06.12.2018) W !P O PCT
(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 2018/223101 Al 06 December 2018 (06.12.2018) W !P O PCT (51) International Patent Classification: (71) Applicant: JUNO THERAPEUTICS, INC. [US/US]; 400 A 61K 35/1 7 (20 15.0 1) A 61P 35/00 (2006 .0 1) Dexter Ave. N., Suite 1200, Seattle, WA 98109 (US). (21) International Application Number: (72) Inventor: ALBERTSON, Tina; 400 Dexter Ave. N., Suite PCT/US2018/035755 1200, Seattle, WA 98109 (US). (22) International Filing Date: (74) Agent: AHN, Sejin et al; Morrison & Foerster LLP, 1253 1 0 1 June 2018 (01 .06.2018) High Bluff Drive, Suite 100, San Diego, CA 92130-2040 (US). (25) Filing Language: English (81) Designated States (unless otherwise indicated, for every (26) Publication Language: English kind of national protection available): AE, AG, AL, AM, (30) Priority Data: AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, 62/5 14,774 02 June 2017 (02.06.2017) US CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, 62/5 15,530 05 June 2017 (05.06.2017) US DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, 62/521,366 16 June 2017 (16.06.2017) u s HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, 62/527,000 29 June 2017 (29.06.2017) u s KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, 62/549,938 24 August 2017 (24.08.2017) u s MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 62/580,425 0 1 November 2017 (01 .11.2017) u s OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, 62/593,871 0 1 December 2017 (01 .12.2017) u s SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 62/596,764 08 December 2017 (08.12.2017) u s TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. -
Mitochondrial Metabolism and Cancer
Cell Research (2018) 28:265-280. REVIEW www.nature.com/cr Mitochondrial metabolism and cancer Paolo Ettore Porporato1, *, Nicoletta Filigheddu2, *, José Manuel Bravo-San Pedro3, 4, 5, 6, 7, Guido Kroemer3, 4, 5, 6, 7, 8, 9, Lorenzo Galluzzi3, 10, 11 1Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, 10124 Torino, Italy; 2Department of Translational Medicine, University of Piemonte Orientale, 28100 Novara, Italy; 3Université Paris Descartes/Paris V, Sorbonne Paris Cité, 75006 Paris, France; 4Université Pierre et Marie Curie/Paris VI, 75006 Paris, France; 5Equipe 11 labellisée par la Ligue contre le Cancer, Centre de Recherche des Cordeliers, 75006 Paris, France; 6INSERM, U1138, 75006 Paris, France; 7Meta- bolomics and Cell Biology Platforms, Gustave Roussy Comprehensive Cancer Institute, 94805 Villejuif, France; 8Pôle de Biologie, Hopitâl Européen George Pompidou, AP-HP, 75015 Paris, France; 9Department of Women’s and Children’s Health, Karolinska University Hospital, 17176 Stockholm, Sweden; 10Department of Radiation Oncology, Weill Cornell Medical College, New York, NY 10065, USA; 11Sandra and Edward Meyer Cancer Center, New York, NY 10065, USA Glycolysis has long been considered as the major metabolic process for energy production and anabolic growth in cancer cells. Although such a view has been instrumental for the development of powerful imaging tools that are still used in the clinics, it is now clear that mitochondria play a key role in oncogenesis. Besides exerting central bioen- ergetic functions, mitochondria provide indeed building blocks for tumor anabolism, control redox and calcium ho- meostasis, participate in transcriptional regulation, and govern cell death. Thus, mitochondria constitute promising targets for the development of novel anticancer agents. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Cancer and Mitochondrial Function Revista De La Facultad De Medicina, Vol
Revista de la Facultad de Medicina ISSN: 2357-3848 ISSN: 0120-0011 Universidad Nacional de Colombia Freyre-Bernal, Sofía Isabel; Saavedra-Torres, Jhan Sebastian; Zúñiga-Cerón, Luisa Fernanda; Díaz-Córdoba, Wilmer Jair; Pinzón-Fernández, María Virginia Cancer and mitochondrial function Revista de la Facultad de Medicina, vol. 66, no. 1, 2018, January-March, pp. 83-86 Universidad Nacional de Colombia DOI: 10.15446/revfacmed.v66n1.59898 Available in: http://www.redalyc.org/articulo.oa?id=576364217013 How to cite Complete issue Scientific Information System Redalyc More information about this article Network of Scientific Journals from Latin America and the Caribbean, Spain and Journal's webpage in redalyc.org Portugal Project academic non-profit, developed under the open access initiative Rev. Fac. Med. 2017 Vol. 66 No. 1: 83-6 83 ARTÍCULO DE REFLEXIÓN DOI: http://dx.doi.org/10.15446/revfacmed.v66n1.59898 Cancer and mitochondrial function El cáncer en la función mitocondrial Recibido: 3/9/2016. Aceptado: 28/10/2016. Sofía Isabel Freyre-Bernal1 • Jhan Sebastian Saavedra-Torres2,3 • Luisa Fernanda Zúñiga-Cerón2,3 • Wilmer Jair Díaz-Córdoba2,3 • María Virginia Pinzón-Fernández4 1 Universidad del Cauca - Faculty of Health Sciences - Department of Physiological Sciences - Popayán - Colombia. 2 Corporación Del Laboratorio al Campo - Research Seedling Unit - Popayán - Colombia. 3 Universidad del Cauca - Faculty of Health Sciences - Health Research Group - Popayán - Colombia. 4 Universidad del Cauca - Faculty of Health Sciences - Internal Medicine Department - Popayán - Colombia. Corresponding author: Jhan Sebastian Saavedra-Torres. Health Research Group, Faculty of Health Sciences, Universidad del Cauca. Colombia, Cauca. Carrera 6 Nº 13N-50 de Popayán, sector de La Estancia. -
Type of the Paper (Article
Supplementary Material A Proteomics Study on the Mechanism of Nutmeg-induced Hepatotoxicity Wei Xia 1, †, Zhipeng Cao 1, †, Xiaoyu Zhang 1 and Lina Gao 1,* 1 School of Forensic Medicine, China Medical University, Shenyang 110122, P. R. China; lessen- [email protected] (W.X.); [email protected] (Z.C.); [email protected] (X.Z.) † The authors contributed equally to this work. * Correspondence: [email protected] Figure S1. Table S1. Peptide fraction separation liquid chromatography elution gradient table. Time (min) Flow rate (mL/min) Mobile phase A (%) Mobile phase B (%) 0 1 97 3 10 1 95 5 30 1 80 20 48 1 60 40 50 1 50 50 53 1 30 70 54 1 0 100 1 Table 2. Liquid chromatography elution gradient table. Time (min) Flow rate (nL/min) Mobile phase A (%) Mobile phase B (%) 0 600 94 6 2 600 83 17 82 600 60 40 84 600 50 50 85 600 45 55 90 600 0 100 Table S3. The analysis parameter of Proteome Discoverer 2.2. Item Value Type of Quantification Reporter Quantification (TMT) Enzyme Trypsin Max.Missed Cleavage Sites 2 Precursor Mass Tolerance 10 ppm Fragment Mass Tolerance 0.02 Da Dynamic Modification Oxidation/+15.995 Da (M) and TMT /+229.163 Da (K,Y) N-Terminal Modification Acetyl/+42.011 Da (N-Terminal) and TMT /+229.163 Da (N-Terminal) Static Modification Carbamidomethyl/+57.021 Da (C) 2 Table S4. The DEPs between the low-dose group and the control group. Protein Gene Fold Change P value Trend mRNA H2-K1 0.380 0.010 down Glutamine synthetase 0.426 0.022 down Annexin Anxa6 0.447 0.032 down mRNA H2-D1 0.467 0.002 down Ribokinase Rbks 0.487 0.000 -
Folate Cycle Enzyme MTHFD1L Confers Metabolic Advantages in Hepatocellular Carcinoma
RESEARCH ARTICLE The Journal of Clinical Investigation Folate cycle enzyme MTHFD1L confers metabolic advantages in hepatocellular carcinoma Derek Lee,1 Iris Ming-Jing Xu,1 David Kung-Chun Chiu,1 Robin Kit-Ho Lai,1 Aki Pui-Wah Tse,1 Lynna Lan Li,1 Cheuk-Ting Law,1 Felice Ho-Ching Tsang,1 Larry Lai Wei,1 Cerise Yuen-Ki Chan,1 Chun-Ming Wong,1,2 Irene Oi-Lin Ng,1,2 and Carmen Chak-Lui Wong1,2 1Department of Pathology and 2State Key Laboratory for Liver Research, The University of Hong Kong, Hong Kong, China. Cancer cells preferentially utilize glucose and glutamine, which provide macromolecules and antioxidants that sustain rapid cell division. Metabolic reprogramming in cancer drives an increased glycolytic rate that supports maximal production of these nutrients. The folate cycle, through transfer of a carbon unit between tetrahydrofolate and its derivatives in the cytoplasmic and mitochondrial compartments, produces other metabolites that are essential for cell growth, including nucleotides, methionine, and the antioxidant NADPH. Here, using hepatocellular carcinoma (HCC) as a cancer model, we have observed a reduction in growth rate upon withdrawal of folate. We found that an enzyme in the folate cycle, methylenetetrahydrofolate dehydrogenase 1–like (MTHFD1L), plays an essential role in support of cancer growth. We determined that MTHFD1L is transcriptionally activated by NRF2, a master regulator of redox homeostasis. Our observations further suggest that MTHFD1L contributes to the production and accumulation of NADPH to levels that are sufficient to combat oxidative stress in cancer cells. The elevation of oxidative stress through MTHFD1L knockdown or the use of methotrexate, an antifolate drug, sensitizes cancer cells to sorafenib, a targeted therapy for HCC. -
Physiologically Based Pharmacokinetic Modeling in Regulatory Decision‐Making at the European Medicines Agency
ARTICLES Physiologically Based Pharmacokinetic Modeling in Regulatory Decision-Making at the European Medicines Agency E Luzon1, K Blake1, S Cole2,3,4, A Nordmark4,5, C Versantvoort3,6 and E Gil Berglund3,5 Physiologically based pharmacokinetic (PBPK) modeling is a valuable tool in drug development and regulatory assessment, as it offers the opportunity to simulate the pharmacokinetics of a compound, with a mechanistic understanding, in a variety of populations and situations. This work reviews the use and impact of such modeling in selected regulatory procedures submitted to the European Medicines Agency (EMA) before the end of 2015, together with its subsequent reflection in public documents relating to the assessment of these procedures. It is apparent that the reference to PBPK modeling in regulatory public documents underrepresents its use. A positive trend over time of the number of PBPK models submitted is shown, and in a number of cases the results of these may impact the decision- making process or lead to recommendations in the product labeling. These results confirm the need for regulatory guidance in this field, which is currently under development by the EMA. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE WHAT THIS STUDY ADDS TO OUR KNOWLEDGE TOPIC? þ This is the first work presenting the situation in the Europe- þ PBPK is a cutting-edge technology that is increasingly being an Union and confirms what was detected by the FDA is a applied to drug development to address various aspects related global trend. to clinical pharmacology. Previous works by the FDA acknowl- HOW THIS MIGHT CHANGE CLINICAL PHARMA- edge its use in regulatory submissions in the US, but so far there COLOGY OR TRANSLATIONAL SCIENCE is no analysis of the European perspective. -
Differential Requirements for Tousled-Like Kinases 1 and 2 in Mammalian Development
Cell Death and Differentiation (2017) 24, 1872–1885 & 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved 1350-9047/17 www.nature.com/cdd Differential requirements for Tousled-like kinases 1 and 2 in mammalian development Sandra Segura-Bayona1,8, Philip A Knobel1,8, Helena González-Burón1,8, Sameh A Youssef2,3, Aida Peña-Blanco1, Étienne Coyaud4,5, Teresa López-Rovira1, Katrin Rein1, Lluís Palenzuela1, Julien Colombelli1, Stephen Forrow1, Brian Raught4,5, Anja Groth6, Alain de Bruin2,7 and Travis H Stracker*,1 The regulation of chromatin structure is critical for a wide range of essential cellular processes. The Tousled-like kinases, TLK1 and TLK2, regulate ASF1, a histone H3/H4 chaperone, and likely other substrates, and their activity has been implicated in transcription, DNA replication, DNA repair, RNA interference, cell cycle progression, viral latency, chromosome segregation and mitosis. However, little is known about the functions of TLK activity in vivo or the relative functions of the highly similar TLK1 and TLK2 in any cell type. To begin to address this, we have generated Tlk1- and Tlk2-deficient mice. We found that while TLK1 was dispensable for murine viability, TLK2 loss led to late embryonic lethality because of placental failure. TLK2 was required for normal trophoblast differentiation and the phosphorylation of ASF1 was reduced in placentas lacking TLK2. Conditional bypass of the placental phenotype allowed the generation of apparently healthy Tlk2-deficient mice, while only the depletion of both TLK1 and TLK2 led to extensive genomic instability, indicating that both activities contribute to genome maintenance. Our data identifies a specific role for TLK2 in placental function during mammalian development and suggests that TLK1 and TLK2 have largely redundant roles in genome maintenance. -
Drug Name Plate Number Well Location % Inhibition, Screen Axitinib 1 1 20 Gefitinib (ZD1839) 1 2 70 Sorafenib Tosylate 1 3 21 Cr
Drug Name Plate Number Well Location % Inhibition, Screen Axitinib 1 1 20 Gefitinib (ZD1839) 1 2 70 Sorafenib Tosylate 1 3 21 Crizotinib (PF-02341066) 1 4 55 Docetaxel 1 5 98 Anastrozole 1 6 25 Cladribine 1 7 23 Methotrexate 1 8 -187 Letrozole 1 9 65 Entecavir Hydrate 1 10 48 Roxadustat (FG-4592) 1 11 19 Imatinib Mesylate (STI571) 1 12 0 Sunitinib Malate 1 13 34 Vismodegib (GDC-0449) 1 14 64 Paclitaxel 1 15 89 Aprepitant 1 16 94 Decitabine 1 17 -79 Bendamustine HCl 1 18 19 Temozolomide 1 19 -111 Nepafenac 1 20 24 Nintedanib (BIBF 1120) 1 21 -43 Lapatinib (GW-572016) Ditosylate 1 22 88 Temsirolimus (CCI-779, NSC 683864) 1 23 96 Belinostat (PXD101) 1 24 46 Capecitabine 1 25 19 Bicalutamide 1 26 83 Dutasteride 1 27 68 Epirubicin HCl 1 28 -59 Tamoxifen 1 29 30 Rufinamide 1 30 96 Afatinib (BIBW2992) 1 31 -54 Lenalidomide (CC-5013) 1 32 19 Vorinostat (SAHA, MK0683) 1 33 38 Rucaparib (AG-014699,PF-01367338) phosphate1 34 14 Lenvatinib (E7080) 1 35 80 Fulvestrant 1 36 76 Melatonin 1 37 15 Etoposide 1 38 -69 Vincristine sulfate 1 39 61 Posaconazole 1 40 97 Bortezomib (PS-341) 1 41 71 Panobinostat (LBH589) 1 42 41 Entinostat (MS-275) 1 43 26 Cabozantinib (XL184, BMS-907351) 1 44 79 Valproic acid sodium salt (Sodium valproate) 1 45 7 Raltitrexed 1 46 39 Bisoprolol fumarate 1 47 -23 Raloxifene HCl 1 48 97 Agomelatine 1 49 35 Prasugrel 1 50 -24 Bosutinib (SKI-606) 1 51 85 Nilotinib (AMN-107) 1 52 99 Enzastaurin (LY317615) 1 53 -12 Everolimus (RAD001) 1 54 94 Regorafenib (BAY 73-4506) 1 55 24 Thalidomide 1 56 40 Tivozanib (AV-951) 1 57 86 Fludarabine -
S42003-019-0587-Z.Pdf
Corrected: Author Correction ARTICLE https://doi.org/10.1038/s42003-019-0587-z OPEN High-resolution crystal structure of human asparagine synthetase enables analysis of inhibitor binding and selectivity Wen Zhu 1,10, Ashish Radadiya 1, Claudine Bisson2,10, Sabine Wenzel 3, Brian E. Nordin4,11, 1234567890():,; Francisco Martínez-Márquez3, Tsuyoshi Imasaki 3,5, Svetlana E. Sedelnikova2, Adriana Coricello 1,6,7, Patrick Baumann 1, Alexandria H. Berry8, Tyzoon K. Nomanbhoy4, John W. Kozarich 4, Yi Jin 1, David W. Rice 2, Yuichiro Takagi 3 & Nigel G.J. Richards 1,9 Expression of human asparagine synthetase (ASNS) promotes metastatic progression and tumor cell invasiveness in colorectal and breast cancer, presumably by altering cellular levels of L-asparagine. Human ASNS is therefore emerging as a bona fide drug target for cancer therapy. Here we show that a slow-onset, tight binding inhibitor, which exhibits nanomolar affinity for human ASNS in vitro, exhibits excellent selectivity at 10 μM concentration in HCT- 116 cell lysates with almost no off-target binding. The high-resolution (1.85 Å) crystal structure of human ASNS has enabled us to identify a cluster of negatively charged side chains in the synthetase domain that plays a key role in inhibitor binding. Comparing this structure with those of evolutionarily related AMP-forming enzymes provides insights into intermolecular interactions that give rise to the observed binding selectivity. Our findings demonstrate the feasibility of developing second generation human ASNS inhibitors as lead compounds for the discovery of drugs against metastasis. 1 School of Chemistry, Cardiff University, Cardiff, UK. 2 Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, UK. -
Related Kinases (VRK) Bound to Small-Molecule Inhibitors Identifies Different P-Loop Conformations
Structural characterization of human Vaccinia- Related Kinases (VRK) bound to small-molecule inhibitors identifies different P-loop conformations Rafael M. Couñago1,2*, Charles K. Allerston3, Pavel Savitsky3, Hatylas Azevedo4, Paulo H Godoi1,5, Carrow I. Wells6, Alessandra Mascarello4, Fernando H. de Souza Gama4, Katlin B. Massirer1,2, William J. Zuercher6, Cristiano R.W. Guimarães4 and Opher Gileadi1,3 1. Structural Genomics Consortium, Universidade Estadual de Campinas — UNICAMP, Campinas, SP, Brazil. 2. Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, Campinas, SP, Brazil. 3. Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, University of Oxford, UK. 4. Aché Laboratórios Farmacêuticos SA, Guarulhos, SP, Brazil. 5. Department of Biochemistry and Tissue Biology, Institute of Biology, State University of Campinas, Campinas, Brazil. 6. Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC, USA. *Correspondence to Rafael M. Couñago: [email protected] (RMC) Supplementary information 1 SUPPLEMENTARY METHODS PKIS results analyses - hierarchical cluster analysis (HCL) A hierarchical clustering (HCL) analysis was performed to group kinases based on their inhibition patterns across the compounds. The average distance clustering method was employed, using sample tree selection and sample leaf order optimization. The distance metric used was the Pearson correlation and the HCL analysis was performed in the TmeV software 1. SUPPLEMENTARY REFERENCES 1 Saeed, A. I. et al. TM4: a free, open-source system for microarray data management and analysis. BioTechniques 34, 374-378, (2003). SUPPLEMENTARY FIGURES LEGENDS Supplementary Figure S1: Hierarchical clustering analysis of PKIS data. Hierarchical clustering analysis of PKIS data. -
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