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Proceedings of: The 6th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2013). Took place 2013, December 11-12, in Edinburgh, UK. The evnt Web site http://www.swat4ls.org/workshops/edinburgh2013/ ; Drug-drug interactions form a significant risk group for adverse effects associ-ated with pharmaceutical treatment. These interactions are often reported in the literature, however, they are sparsely represented in machine-readable re-sources, such as online databases, thesauri or ontologies. These knowledge sources play a pivotal role in Natural Language Processing (NLP) systems since they provide a knowledge representation about the world or a particular do-main. While ontologies for drugs and their effects have proliferated in recent years, there is no ontology capable of describing and categorizing drug-drug in-teractions. Moreover, there is no artifact that represents all the possible mecha-nisms that can lead to a DDI. To fill this gap we propose DINTO, an ontology for drug-drug interactions and their mechanisms. In this paper we describe the classes, relationships and overall structure of DINTO. The ontology is free for use and available at https://code.google.com/p/dinto/ ; This work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542], by the Spanish Ministry of Education under the project MULTIMEDICA [TIN2010-20644-C03-01] and by the European Commission Seventh Framework Programme under the project TrendMiner_Enlarged (EU FP7-ICT 612336). ; Publicado

Abstract Background Prescription writing is a process which transfers the therapeutic message from the prescriber to the patient through the pharmacist. Prescribing errors, drug duplication and potential drug-drug interactions (pDDI) in prescriptions lead to medication error. Assessment of the above was made in prescriptions dispensed at State Pharmaceutical Corporation (SPC), Anuradhapura, Sri Lanka. Methods A cross sectional study was conducted. Drugs were classified according to the WHO anatomical, therapeutic chemical classification system. A three point Likert scale, a checklist and Medscape online drug interaction checker were used to assess legibility, completeness and pDDIs respectively. Results Thousand prescriptions were collected. Majority were hand written (99.8 %) and from the private sector (73 %). The most frequently prescribed substance and subgroup were atorvastatin (4 %, n = 3668) and proton pump inhibitors (7 %, n = 3668) respectively. Out of the substances prescribed from the government and private sectors, 59 and 50 % respectively were available in the national list of essential medicines, Sri Lanka. Patients address (5 %), Sri Lanka Medical Council (SLMC) registration number (35 %), route (7 %), generic name (16 %), treatment symbol (48 %), diagnosis (41 %) and refill information (6 %) were seen in less than half of the prescriptions. Most were legible with effort (65 %) and illegibility was seen in 9 %. There was significant difference in omission and/or errors of generic name ( P = 0.000), dose ( P = 0.000), SLMC registration number ( P = 0.000), and in evidence of pDDI ( P = 0.009) with regards to the sector of prescribing. The commonest subgroup involved in duplication was non-steroidal anti-inflammatory drugs (NSAIDs) (43 %; 56/130). There were 1376 potential drug interactions (466/887 prescriptions). Most common pair causing pDDI was aspirin with losartan (4 %, n = 1376). Conclusion Atorvastatin was the most frequently prescribed substance. Fifteen percent of the prescriptions originate from government sector. SLMC registration number and trade names were seen more in prescriptions originating from the private sector. Most prescriptions were legible with effort. NSAIDs were the commonest implicated in drug class duplication. .

Title from cover. ; Index to U.S. government periodicals ; Excerpta medica. ; Legal resource index ; Mode of access: Internet.

Report A: Letter from the Chairperson, Membership, Terms of Reference, Glossary and Acronyms Report B: Executive Summary and ecommendations: 1. Introduction 2. Trends in the Use of Illicit Drugs in the Northern Territory 3. Prevention 4. Drug Treatment in the Northern Territory. Report C: 5. Treatment - The Role and Practical Application of Pharmacotherapies 6. Priority Groups Appendix A - Illicit Drug Taskforce Consultation Report Appendix B - Summary of Submissions to Taskforce Appendix C - Current Services and Strategies ; The formation of the Taskforce on Illicit Drugs was announced by the Minister for Health and Community Services, Jane Aagaard MLA, in November 2001. It was established to advise the Government on strategies to try to stop the harmful spread of drugs, to break the cycle of crime and to help users and their families on the road to better health.

Report A: Letter from the Chairperson, Membership, Terms of Reference, Glossary and Acronyms Report B: Executive Summary and ecommendations: 1. Introduction 2. Trends in the Use of Illicit Drugs in the Northern Territory 3. Prevention 4. Drug Treatment in the Northern Territory. Report C: 5. Treatment - The Role and Practical Application of Pharmacotherapies 6. Priority Groups Appendix A - Illicit Drug Taskforce Consultation Report Appendix B - Summary of Submissions to Taskforce Appendix C - Current Services and Strategies ; The formation of the Taskforce on Illicit Drugs was announced by the Minister for Health and Community Services, Jane Aagaard MLA, in November 2001. It was established to advise the Government on strategies to try to stop the harmful spread of drugs, to break the cycle of crime and to help users and their families on the road to better health. ; Date:2001

Report A: Letter from the Chairperson, Membership, Terms of Reference, Glossary and Acronyms Report B: Executive Summary and ecommendations: 1. Introduction 2. Trends in the Use of Illicit Drugs in the Northern Territory 3. Prevention 4. Drug Treatment in the Northern Territory. Report C: 5. Treatment - The Role and Practical Application of Pharmacotherapies 6. Priority Groups Appendix A - Illicit Drug Taskforce Consultation Report Appendix B - Summary of Submissions to Taskforce Appendix C - Current Services and Strategies ; The formation of the Taskforce on Illicit Drugs was announced by the Minister for Health and Community Services, Jane Aagaard MLA, in November 2001. It was established to advise the Government on strategies to try to stop the harmful spread of drugs, to break the cycle of crime and to help users and their families on the road to better health.

Testimony issued by the Government Accountability Office with an abstract that begins "Several federal programs help pay for or reduce the costs of prescription drugs for eligible individuals and entities. Three examples are the Medicaid drug rebate program, part of the joint federal-state Medicaid program that finances medical services for certain low-income people; the 340B drug pricing program, which provides discounted drug prices to certain eligible entities such as community health centers; and the Medicare Part D program, which provides a Medicare drug benefit for the elderly and certain disabled people. The price information drug manufacturers report under these federal programs affects related federal spending. Spending is also affected by the extent to which federal oversight ensures the accuracy of this information. GAO was asked to provide information related to the oversight of prescription drug pricing practices that affect these federal programs. This testimony focuses on the oversight of drug pricing related to the three programs and the implications for future congressional oversight. This testimony is based on recent GAO reports examining these programs and related work by the Department of Health and Human Services Office of Inspector General and others."

This report summarizes the Authorization of the National Youth Anti-Drug Media Campaign, a multi-media federal program to persuade America's youth not to use drugs, expired at the end of FY2002. The 108th Congress is expected to consider the reauthorization of the campaign as part of the reauthorization of the Office of National Drug Control Policy (ONDCP). The campaign's effectiveness has been questioned, and the program has engendered its share of controversy.

This report examines prescription drug abuse in the United States. An estimated 6.8 million individuals currently abuse prescription drugs in the United States. Unlike policy on street drugs, federal policy on prescription drug abuse is complicated by the need to maintain access to prescription controlled substances (PCS) for legitimate medical use. The federal government has several roles in reducing prescription drug abuse.

Xiangyi Cui,1 Yuhong Lü,1,2 Changwu Yue1,2 1Key Laboratory of Microbial Drugs Innovation and Transformation of Yan'an, School of Basic Medicine, Yan'an University, Yan'an, 716000, Shaanxi, People's Republic of China; 2Shaanxi Engineering & Technological Research Center for Conversation & Utilization of Regional Biological Resources, Yan'an University, Yan'an, 716000, Shaanxi, People's Republic of ChinaCorrespondence: Yuhong Lü; Changwu YueYan'an University, No. 580 Shengdi Road, Baota District, Yan'an, 716000, Shaanxi, People's Republic of ChinaTel/Fax +86 911-2332-067Email yuhonglyu@126.com; changwuyue@126.comAbstract: Bacterial resistance has become increasingly serious because of the widespread use and abuse of antibiotics. In particular, the emergence of multidrug-resistant bacteria has posed a serious threat to human public health and attracted the attention of the World Health Organization (WHO) and the governments of various countries. Therefore, the establishment of measures against bacterial resistance and the discovery of new antibacterial drugs are increasingly urgent to better contain the emergence of bacterial resistance and provide a reference for the development of new antibacterial drugs. In this review, we discuss some antibiotic drugs that have been approved for clinical use and a partial summary of the meaningful research results of anti-drug resistant bacterial drugs in different fields, including the antibiotic drugs approved by the FDA from 2015 to 2020, the potential drugs against drug-resistant bacteria, the new molecules synthesized by chemical modification, combination therapy, drug repurposing, immunotherapy and other therapies.Keywords: multidrug-resistant bacteria, antimicrobial resistance, novel antibiotics, antibiotics, antibiotic discovery

AIM: To study drug-drug interactions in myocardial infarction patients. METHODOLOGY: A prospective interventional study was done on myocardial infarction with drug interactions patients admitted in tertiary government and corporate hospital, Visakhapatnam Andhra Pradesh, India. Information regarding the gender, risk factors, type of drug interactions as major, moderate, minor were recorded in a standard questionnaire (case report form).RESULTS: A total of 100 cases were included after excluding missing data. Out of 100 cases,62 were male and 38 were female.Myocardial infarction in patients in which we assessed risk factors among them were HTN-67%,previous history of CVD-66%,family history-60%,Hyperlipidemia-40%,Diabetes milletus-38%,smoking-64%,Drug interactions were Major,Moderate,Minor.Major interactions were 24, Moderate interactions were 318, Minor interactions were 91.CONCLUSION: Based on the above data risk factors mostly seen in HTN and previous history of CVD, drug interactions mostly observed were moderate drug interactions.

Testimony issued by the General Accounting Office with an abstract that begins "Pursuant to a congressional request, GAO discussed the role of the Department of Defense (DOD) in reducing the supply of illegal drugs entering the United States, focusing on: (1) the decline in DOD's aerial and maritime support allocated to counterdrug activities from fiscal years 1992 through 1999 and some of the consequences and reasons for the decline; (2) the obstacles DOD faces in helping foreign governments counter illegal drug activities; and (3) DOD's counterdrug strategy and the need for performance measures to judge its counterdrug program effectiveness."

Objective: To develop and implement an online bulletin intended to provide information on locally available medicinal products undergoing variation in their Summary of Product Characteristics (SmPC) and inclusions of medicinal products in the Government Formulary List (GFL) and to evaluate the usefulness of the bulletin among healthcare professionals and students. Method: Lists of locally available medicines that had undergone variations and medicinal products added to the GFL during a 6-month time frame were compiled. Additional information was obtained from regulatory authorities and local agents. A concise article was written on each drug, reviewed by a panel of experts and subsequently published in the online bulletin. Following completion of the contents and layout, the material was uploaded on the website of the Department of Pharmacy, University of Malta. A pilot study was carried out to identify shortcomings in the online bulletin design. The launch of the online bulletin started the promotional process which was mainly focused on electronic mail marketing. The data obtained from the validated questionnaire was coded and analysed using SPSS® v.20 and Microsoft® Excel Vista®. Key findings: Sixty seven per cent (n=223) of the respondents returned the questionnaire. The online bulletin was evaluated by 27 medical practitioners, 34 pharmacists, 37 medical students and 51 pharmacy students; of which 35% (n=52) were male and 65% (n=97) were female. The respondents agreed that the bulletin was up-to-date, clear and concise (91%, n=136), user-friendly (97%, n=144), useful (95%, n=142), well-designed (91%, n=136) and easy to access (87%, n=130). Ninety seven per cent (n=145) of the respondents stated that the information present in the bulletin was new to them, whilst 97% (n=144) agreed that the bulletin helped to keep them informed. Conclusion: The online bulletin provided an accessible means to deliver unbiased information about the introduction of recent medicinal products on the GFL and variations in SmPCs of products available on the local market. It was positively received by both healthcare professionals and students. ; peer-reviewed

The poster at: The Sixth International Biocuration Conference (Biocuration 2013), took at April 7–10, 2013 in Churchill College, Cambridge, UK. The event web site in: http://www.ebi.ac.uk/biocuration2013/content/home ; Ontologies are useful tools in text miming reserach tasks as a source of specialized vocabulary of terms and relationships in a given domain. Furthermore, formal knowledge representation propvieded by ontologies can be applied for new knowledge inference, wich can be exploited for biomedical research purposes. Drug-drug interactions (DDIs) are common adverse drug ractions having an important impact on patient safety and healthcare cost. ; This work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542], by the Spanish Ministry of Education under the project MULTIMEDICA [TIN2010-20644-C03-01]. ; Publicado

Drug-drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning. This technique aims to automatically learn the best feature representation from the input data for a given task. The purpose of this paper is to examine whether a convolutional neural network (CNN), which only uses word embeddings as input features, can be applied successfully to classify DDIs from biomedical texts. Proposed herein, is a CNN architecture with only one hidden layer, thus making the model more computationally efficient, and we perform detailed experiments in order to determine the best settings of the model. The goal is to determine the best parameter of this basic CNN that should be considered for future research. The experimental results show that the proposed approach is promising because it attained the second position in the 2013 rankings of the DDI extraction challenge. However, it obtained worse results than previous works using neural networks with more complex architectures. ; Research Program of the Ministry of Economy, Industry and Competitiveness, Government of Spain [eGovernAbility-Access project TIN2014-52665-C2-2-R].