The Effects of Combination Antibiotic Therapy on Methicillin-Resistant Staphylococcus Aureus Presented by Kasra Nick Fallah In
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Pharmacogenetic Testing: a Tool for Personalized Drug Therapy Optimization
pharmaceutics Review Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization Kristina A. Malsagova 1,* , Tatyana V. Butkova 1 , Arthur T. Kopylov 1 , Alexander A. Izotov 1, Natalia V. Potoldykova 2, Dmitry V. Enikeev 2, Vagarshak Grigoryan 2, Alexander Tarasov 3, Alexander A. Stepanov 1 and Anna L. Kaysheva 1 1 Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; [email protected] (T.V.B.); [email protected] (A.T.K.); [email protected] (A.A.I.); [email protected] (A.A.S.); [email protected] (A.L.K.) 2 Institute of Urology and Reproductive Health, Sechenov University, 119992 Moscow, Russia; [email protected] (N.V.P.); [email protected] (D.V.E.); [email protected] (V.G.) 3 Institute of Linguistics and Intercultural Communication, Sechenov University, 119992 Moscow, Russia; [email protected] * Correspondence: [email protected]; Tel.: +7-499-764-9878 Received: 2 November 2020; Accepted: 17 December 2020; Published: 19 December 2020 Abstract: Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician’s ability to understand the obtained results in a standardized way for each particular patient. -
Plague Plague
Disease Planning Guide - Plague Mass Post Exposure Prophylaxis I. Disease-specific guidance: The following guide should only be used in events where a plague epidemic has been identified, an Emergency Use Authorization from the Food and Drug Administration has been issued and a state and local emergency declaration has been issued by the proper authorities. Model Standing Order – Pg 1 Clients will arrive at dispensing sites with a voucher indicating which medication, if any, they should receive. Specific guidance related to dispensing is shown in red . Model Standing Order – Pg 2 Clients that present this voucher will not receive medication for the individual named on the form. Dispensers may collect “Do Not Dispense Medication” vouchers for records purposes. Model Standing Order – Pg 3 II. PERSONS FOR WHOM PROPHYLAXIS MAY BE ORDERED Persons who have a confirmed or suspect exposure to Yersinia pestis, the causative agent of plague, as determined by the local or State Health Officer. Table 1: Recommended Post-Exposure Prophylaxis for Inhalational Plague Patient Category Therapy Recommendation Duration 10 days Adults Doxycycline , 100 mg PO BID or Ciprofloxacin , 500 mg PO BID 10 days Ciprofloxacin , 15 mg/kg PO BID, max 500 mg/dose or Doxycycline : >45 kg: 100 mg PO BID Children <45 kg: 2.2 mg/kg PO BID ***************************************** If susceptibility to penicillin has been confirmed: Amoxicillin : >20 kg: 500 mg PO TID <20 kg: 80 mg/kg/day PO divided TID 10 days Doxycycline , 100 mg PO BID or Pregnant women Ciprofloxacin , 500 mg PO BID ***************************************** If susceptibility to penicillin has been confirmed: Amoxicillin 500 mg PO TID 10 days Immunocompromised Doxycycline , 100 mg PO BID or Ciprofloxacin , 500 mg PO BID Adapted from: Working Group on Civilian Biodefense. -
Pharmacogenomic Biomarkers in Docetaxel Treatment of Prostate Cancer: from Discovery to Implementation
G C A T T A C G G C A T genes Review Pharmacogenomic Biomarkers in Docetaxel Treatment of Prostate Cancer: From Discovery to Implementation Reka Varnai 1,2, Leena M. Koskinen 3, Laura E. Mäntylä 3, Istvan Szabo 4,5, Liesel M. FitzGerald 6 and Csilla Sipeky 3,* 1 Department of Primary Health Care, University of Pécs, Rákóczi u 2, H-7623 Pécs, Hungary 2 Faculty of Health Sciences, Doctoral School of Health Sciences, University of Pécs, Vörösmarty u 4, H-7621 Pécs, Hungary 3 Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20520 Turku, Finland 4 Institute of Sport Sciences and Physical Education, University of Pécs, Ifjúság útja 6, H-7624 Pécs, Hungary 5 Faculty of Sciences, Doctoral School of Biology and Sportbiology, University of Pécs, Ifjúság útja 6, H-7624 Pécs, Hungary 6 Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania 7000, Australia * Correspondence: csilla.sipeky@utu.fi Received: 17 June 2019; Accepted: 5 August 2019; Published: 8 August 2019 Abstract: Prostate cancer is the fifth leading cause of male cancer death worldwide. Although docetaxel chemotherapy has been used for more than fifteen years to treat metastatic castration resistant prostate cancer, the high inter-individual variability of treatment efficacy and toxicity is still not well understood. Since prostate cancer has a high heritability, inherited biomarkers of the genomic signature may be appropriate tools to guide treatment. In this review, we provide an extensive overview and discuss the current state of the art of pharmacogenomic biomarkers modulating docetaxel treatment of prostate cancer. This includes (1) research studies with a focus on germline genomic biomarkers, (2) clinical trials including a range of genetic signatures, and (3) their implementation in treatment guidelines. -
Pharmacogenomic Testing for Selected Conditions Final Evidence Report
Pharmacogenomic testing for selected conditions Final evidence report December 9, 2016 Health Technology Assessment Program (HTA) Washington State Health Care Authority PO Box 42712 Olympia, WA 98504-2712 (360) 725-5126 www.hca.wa.gov/about-hca/health-technology-assessment [email protected] Pharmacogenomic Testing for Selected Conditions A Health Technology Assessment Prepared for Washington State Healthcare Authority FINAL REPORT December 9, 2016 Acknowledgement This report was prepared by: Hayes, Inc. 157 S. Broad Street Suite 200 Lansdale, PA 19446 P: 215.855.0615 F: 215.855.5218 This report is intended to provide research assistance and general information only. It is not intended to be used as the sole basis for determining coverage policy or defining treatment protocols or medical modalities, nor should it be construed as providing medical advice regarding treatment of an individual’s specific case. Any decision regarding claims eligibility or benefits, or acquisition or use of a health technology is solely within the discretion of your organization. Hayes, Inc. assumes no responsibility or liability for such decisions. Hayes employees and contractors do not have material, professional, familial, or financial affiliations that create actual or potential conflicts of interest related to the preparation of this report. WA – Health Technology Assessment December 9, 2016 Table of Contents EVIDENCE SUMMARY ................................................................................................................................... -
Current Antimalarial Therapies and Advances in the Development of Semi-Synthetic Artemisinin Derivatives
Anais da Academia Brasileira de Ciências (2018) 90(1 Suppl. 2): 1251-1271 (Annals of the Brazilian Academy of Sciences) Printed version ISSN 0001-3765 / Online version ISSN 1678-2690 http://dx.doi.org/10.1590/0001-3765201820170830 www.scielo.br/aabc | www.fb.com/aabcjournal Current Antimalarial Therapies and Advances in the Development of Semi-Synthetic Artemisinin Derivatives LUIZ C.S. PINHEIRO1, LÍVIA M. FEITOSA1,2, FLÁVIA F. DA SILVEIRA1,2 and NUBIA BOECHAT1 1Fundação Oswaldo Cruz, Instituto de Tecnologia em Fármacos Farmanguinhos, Fiocruz, Departamento de Síntese de Fármacos, Rua Sizenando Nabuco, 100, Manguinhos, 21041-250 Rio de Janeiro, RJ, Brazil 2Universidade Federal do Rio de Janeiro, Programa de Pós-Graduação em Química, Avenida Athos da Silveira Ramos, 149, Cidade Universitária, 21941-909 Rio de Janeiro, RJ, Brazil Manuscript received on October 17, 2017; accepted for publication on December 18, 2017 ABSTRACT According to the World Health Organization, malaria remains one of the biggest public health problems in the world. The development of resistance is a current concern, mainly because the number of safe drugs for this disease is limited. Artemisinin-based combination therapy is recommended by the World Health Organization to prevent or delay the onset of resistance. Thus, the need to obtain new drugs makes artemisinin the most widely used scaffold to obtain synthetic compounds. This review describes the drugs based on artemisinin and its derivatives, including hybrid derivatives and dimers, trimers and tetramers that contain an endoperoxide bridge. This class of compounds is of extreme importance for the discovery of new drugs to treat malaria. Key words: malaria, Plasmodium falciparum, artemisinin, hybrid. -
ARCH-Vet Anresis.Ch
Usage of Antibiotics and Occurrence of Antibiotic Resistance in Bacteria from Humans and Animals in Switzerland Joint report 2013 ARCH-Vet anresis.ch Publishing details © Federal Office of Public Health FOPH Published by: Federal Office of Public Health FOPH Publication date: November 2015 Editors: Federal Office of Public Health FOPH, Division Communicable Diseases. Elisabetta Peduzzi, Judith Klomp, Virginie Masserey Design and layout: diff. Marke & Kommunikation GmbH, Bern FOPH publication number: 2015-OEG-17 Source: SFBL, Distribution of Publications, CH-3003 Bern www.bundespublikationen.admin.ch Order number: 316.402.eng Internet: www.bag.admin.ch/star www.blv.admin.ch/gesundheit_tiere/04661/04666 Table of contents 1 Foreword 4 Vorwort 5 Avant-propos 6 Prefazione 7 2 Summary 10 Zusammenfassung 12 Synthèse 14 Sintesi 17 3 Introduction 20 3.1 Antibiotic resistance 20 3.2 About anresis.ch 20 3.3 About ARCH-Vet 21 3.4 Guidance for readers 21 4 Abbreviations 24 5 Antibacterial consumption in human medicine 26 5.1 Hospital care 26 5.2 Outpatient care 31 5.3 Discussion 32 6 Antibacterial sales in veterinary medicines 36 6.1 Total antibacterial sales for use in animals 36 6.2 Antibacterial sales – pets 37 6.3 Antibacterial sales – food producing animals 38 6.4 Discussion 40 7 Resistance in bacteria from human clinical isolates 42 7.1 Escherichia coli 42 7.2 Klebsiella pneumoniae 44 7.3 Pseudomonas aeruginosa 48 7.4 Acinetobacter spp. 49 7.5 Streptococcus pneumoniae 52 7.6 Enterococci 54 7.7 Staphylococcus aureus 55 Table of contents 1 8 Resistance in zoonotic bacteria 58 8.1 Salmonella spp. -
Drug Combinations: Mathematical Modeling and Networking Methods
pharmaceutics Review Drug Combinations: Mathematical Modeling and Networking Methods Vahideh Vakil † and Wade Trappe *,† Wireless Information Network Laboratory (WINLAB), Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-848-932-0909 † Current address: Technology Centre of New Jersey, 671 Route 1 South, North Brunswick, NJ 08902-3390, USA. Received: 12 February 2019; Accepted: 27 April 2019; Published: 2 May 2019 Abstract: Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. -
Codevelopment of Two Or More New Investigational Drugs for Use in Combination
Guidance for Industry Codevelopment of Two or More New Investigational Drugs for Use in Combination U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) June 2013 Clinical Medical Guidance for Industry Codevelopment of Two or More New Investigational Drugs for Use in Combination Additional copies are available from: Office of Communications Division of Drug Information, WO51, Room 2201 Center for Drug Evaluation and Research Food and Drug Administration 10903 New Hampshire Ave. Silver Spring, MD 20993 Phone: 301-796-3400; Fax: 301-847-8714 [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) June 2013 Clinical Medical Contains Nonbinding Recommendations Table of Contents I. INTRODUCTION............................................................................................................. 1 II. BACKGROUND ............................................................................................................... 2 III. DETERMINING WHETHER CODEVELOPMENT IS AN APPROPRIATE DEVELOPMENT OPTION ........................................................................................................ 2 IV. NONCLINICAL CODEVELOPMENT ......................................................................... 4 A. Demonstrating the Biological Rationale for the Combination...................................................4 -
Guidelines for Atcvet Classification 2012
Guidelines for ATCvet classification 2012 ISSN 1020-9891 ISBN 978-82-8082-479-0 Suggested citation: WHO Collaborating Centre for Drug Statistics Methodology, Guidelines for ATCvet classification 2012. Oslo, 2012. © Copyright WHO Collaborating Centre for Drug Statistics Methodology, Oslo, Norway. Use of all or parts of the material requires reference to the WHO Collaborating Centre for Drug Statistics Methodology. Copying and distribution for commercial purposes is not allowed. Changing or manipulating the material is not allowed. Guidelines for ATCvet classification 14th edition WHO Collaborating Centre for Drug Statistics Methodology Norwegian Institute of Public Health P.O.Box 4404 Nydalen N-0403 Oslo Norway Telephone: +47 21078160 Telefax: +47 21078146 E-mail: [email protected] Website: www.whocc.no Previous editions: 1992: Guidelines on ATCvet classification, 1st edition1) 1995: Guidelines on ATCvet classification, 2nd edition1) 1999: Guidelines on ATCvet classification, 3rd edition1) 2002: Guidelines for ATCvet classification, 4th edition2) 2003: Guidelines for ATCvet classification, 5th edition2) 2004: Guidelines for ATCvet classification, 6th edition2) 2005: Guidelines for ATCvet classification, 7th edition2) 2006: Guidelines for ATCvet classification, 8th edition2) 2007: Guidelines for ATCvet classification, 9th edition2) 2008: Guidelines for ATCvet classification, 10th edition2) 2009: Guidelines for ATCvet classification, 11th edition2) 2010: Guidelines for ATCvet classification, 12th edition2) 2011: Guidelines for ATCvet classification, 13th edition2) 1) Published by the Nordic Council on Medicines 2) Published by the WHO Collaborating Centre for Drug Statistics Methodology Preface The Anatomical Therapeutic Chemical classification system for veterinary medicinal products, ATCvet, has been developed by the Nordic Council on Medicines (NLN) in collaboration with the NLN’s ATCvet working group, consisting of experts from the Nordic countries. -
Motivators and Barriers to Use of Combination Therapies in Patients with HIV Disease
Motivators and Barriers To Use Of Combination Therapies In Patients With HIV Disease Ruthann Richter Maureen Michaels President, Michaels Opinion Research Bruce Carlson Research Associate, Michaels Opinion Research Thomas J. Coates PhD AIDS Research Institute and Center for AIDS Prevention Studies UCSF CAPS Monograph Series, occasional paper #5, January 1998 Center for AIDS Prevention Studies, University of California San Francisco Table of Contents I. Introduction II. Study Design III. Key Findings A. Motivators to Initiate Therapy B. Barriers to Initiation of Therapy C. Other Factors That Influence Decision-Making D. Issues and Concerns of Women IV. Study Implications I. Introduction The successful two-drug combination therapy in 1994 and protease inhibitors in 1995 set the stage for a new era in treatment of HIV disease, creating a burst of optimism over the prospect that HIV might be a controllable disease. Initial studies of protease containing triple-drug regimens suggested that these combinations could, in some cases, slow clinical progression of the disease and prolong the lives of patients. 1, 2 In anecdotal reports, physicians and patients described a kind of "Lazarus" effect in which previously disabled individuals found themselves regaining lost functions, returning to work and planning their futures, instead of preparing for death. There are still many unknowns about these multi-drug regimens, including their durability of effect and how many individuals for whom they will be effective.3, 4 Nonetheless, the drugs have proven quite effective in clinical trials and are helping many people stay alive longer and experience better quality of life while they are alive. We thought it important to understand better why people do and do not take advantage of these therapeutic advances. -
Comparing Treatment Strategies to Reduce Antibiotic Resistance in an In
Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting Daniel C. Angsta,1,2 , Burcu Tepekulea,1,3 , Lei Suna,4, Balazs´ Bogosa,5 , and Sebastian Bonhoeffera aInstitute of Integrative Biology, Department for Environmental System Science, ETH Zurich, 8092 Zurich, Switzerland Edited by Bruce R. Levin, Emory University, Atlanta, GA, and approved February 23, 2021 (received for review November 23, 2020) The rapid rise of antibiotic resistance, combined with the increas- dynamics for infection and patient migration. We complement ing cost and difficulties to develop new antibiotics, calls for this by simulating the same processes using an epidemiological treatment strategies that enable more sustainable antibiotic use. model (Fig. 1A). The development of such strategies, however, is impeded by Our approach mimics a hospital with continuous admission the lack of suitable experimental approaches that allow test- and discharge of patients (Fig. 1B). The central unit represent- ing their effects under realistic epidemiological conditions. Here, ing an individual patient is a well in a microtiter plate, which we present an approach to compare the effect of alternative can either contain only bacterial growth medium (uninfected multidrug treatment strategies in vitro using a robotic liquid- patient/well), or contain sensitive or resistant strains (infected handling platform. We use this framework to study resistance patient/well). As a model organism, we used Escherichia coli evolution and spread implementing epidemiological population MG1655. To increase the likelihood to observe de novo resis- dynamics for treatment, transmission, and patient admission and tance, we used a mutS knockout which exhibits an around discharge, as may be observed in hospitals. -
Pharmacogenetics of Pemetrexed Combination Therapy in Lung Cancer: Pathway Analysis Reveals Novel Toxicity Associations
The Pharmacogenomics Journal (2014) 14, 411–417 & 2014 Macmillan Publishers Limited All rights reserved 1470-269X/14 www.nature.com/tpj ORIGINAL ARTICLE Pharmacogenetics of pemetrexed combination therapy in lung cancer: pathway analysis reveals novel toxicity associations A Corrigan1,6, JL Walker2,6, S Wickramasinghe1, MA Hernandez1, SJ Newhouse3, AA Folarin3, CM Lewis2, JD Sanderson4, J Spicer5 and AM Marinaki1 Identification of polymorphisms that influence pemetrexed tolerability could lead to individualised treatment regimens and improve quality of life. Twenty-eight polymorphisms within eleven candidate genes were genotyped using the Illumina Human Exome v1.1 BeadChip and tested for their association with the clinical outcomes of non-small cell lung cancer and mesothelioma patients receiving pemetrexed/platinum doublet chemotherapy (n ¼ 136). GGH rs11545078 was associated with a reduced incidence of grade X3 toxicity within the first four cycles of therapy (odds ratio (OR) 0.25, P ¼ 0.018), as well as reduced grade X3 haematological toxicity (OR 0.13, P ¼ 0.048). DHFR rs1650697 conferred an increased risk of grade X3 toxicity (OR 2.14, P ¼ 0.034). Furthermore, FOLR3 rs61734430 was associated with an increased likelihood of disease progression at mid-treatment radiological evaluation (OR 4.05, P ¼ 0.023). Polymorphisms within SLC19A1 (rs3788189, rs1051298 and rs914232) were associated with overall survival. This study confirms previous pharmacogenetic associations and identifies novel markers of pemetrexed toxicity. The Pharmacogenomics Journal (2014) 14, 411–417; doi:10.1038/tpj.2014.13; published online 15 April 2014 INTRODUCTION (FPGS).16 Polyglutamation serves for both prolongation of cellular Pemetrexed is a folate antimetabolite approved for the treatment retention of pemetrexed and also increases the affinity of the 5,17 of advanced non-small cell lung cancer (NSCLC) both in drug for its cellular targets.