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1 170 Current Drug Safety, 2012, 7, Implemented Data Mining and Signal Management Systems on Spontaneous Reporting Systems Databases and their Availability to the Scientific Community - A Systematic Review Luís Miguel de Almeida Vieira Lima *,1, Nuno Gonçalo Sales Craveiro Nunes 2,3,8, Pedro Gonçalo Pires da Silva Dias 4,5,6,7 and Francisco Jorge Batel Marques 2,8 1 University of Coimbra, Coimbra, Portugal 2 AIBILI Association for Innovation and Biomedical Research on Light and Image, Coimbra, Portugal 3 Health Sciences Department, University of Aveiro, Aveiro, Portugal 4 Beira Interior University, Covilhã, Portugal 5 Polytechnic Institute of Leiria, Leiria, Portugal 6 Center of Investigation in Human Motricity, Leiria, Portugal 7 Research Center in Sports, Health Sciences and Human Development, Vila Real, Portugal 8 School of Pharmacy, University of Coimbra, Coimbra, Portugal Abstract: Adverse drug reactions spontaneous reporting systems are an important element in worldwide pharmacovigilance, gathering potentially useful information for post-marketing drug safety surveillance. Data mining and signal management systems, providing the capability of reading and interpreting these systems raw data (data that has not been subjected to processing or any other manipulation), improve its analysis process. In order for this analysis to be possible, both data mining and signal management systems and raw data should be available to researchers and the scientific community. The purpose of this work was to provide an overview of the spontaneous reporting systems databases reported in literature as having implemented a data mining and signal management system and the implementation itself, evidencing their availability to researchers. A systematic review was carried out, concluding that they are freely provided to researchers within institutions responsible for maintaining the spontaneous reporting systems, but not to most researchers within the scientific community. Keywords: Data mining, information system, pharmacovigilance, post-marketing safety surveillance, scientific community, signal detection software, spontaneous reporting system. INTRODUCTION Spontaneous Reporting System (SRS) databases, containing suspected adverse drug reactions (ADR) reports, are an important element in the worldwide pharmacovigilance [1]. Despite intrinsic limitations [1-11], the amount of potentially useful information that they contain makes the process of searching for relevant cases among the stored information an important challenge for drug safety researchers. These cases are often named as signals, defined by the World Health Organization as reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously [2], being its searching process known as signalling or signal detection. Due to the increasingly large number of spontaneous reports stored in SRS databases, celerity and efficiency in the process of signalling for relevant cases are being compromised. To overcome the problem, drug regulatory *Address correspondence to this author at the University of Coimbra, Coimbra, Portugal; Tel: ; agencies, market authorization holders (MAH) and the scientific community [2-11] have struggled to develop and enhance data mining and signal management systems (DMSs), systematizing the process of analysing both previously existing and new spontaneous ADR reports. DMSs grew into an essential tool for an efficient detection of potential signals [11]. The objective of the present work is to compile information that identifies and describes the SRS databases reported in the literature as having implemented a DMS and the implementation characteristics themself, giving evidence of their availability to researchers within the scientific community. MATERIALS AND METHODS A Medline search from January 1995 to December 2010 was carried out using the search strategy defined in Table 1. All 210 titles were examined by the first author and reviewed independently by the second author. Relevant abstracts were extracted in order to identify potentially relevant studies. Full papers of potentially relevant studies were obtained and assessed according to the inclusion and exclusion criteria. Studies were selected based on their /12 $ Bentham Science Publishers

2 Adverse Drug Reactions Spontaneous Reporting Systems Current Drug Safety, 2012, Vol. 7, No Table 1. MedLine Search Strategy Information Source (Search Date) Step Number Search Terms Results Pubmed (25/07/2011) #1 Pharmacovigilance 2030 #2 Post marketing surveillance 1108 #3 Adverse drug reaction #4 Adverse event #5 #1 OR #2 OR #3 OR # #6 Signal detection #7 Data mining 7575 #8 Automated signaling 816 #9 #6 OR #7 OR # #10 Software #11 Information System #12 #10 OR # #13 #5 AND #9 AND # #14 Limits: Publication Date from 1995/01/ referral of DMSs implemented on SRS databases [1-14]. Only articles published in English were considered to the present review. Bibliographies of included studies were searched for further relevant studies [15-17]. In complement, institutions were contacted and their websites were accessed, as a way to extend our information sources [18-28]. The selection process is illustrated in Fig. (1). Data were extracted by the first author using a standardized extraction table and checked independently by the second author. All data were review by other authors. Disagreement was resolved by discussion. The extraction table took into account the following variables: Database Data origin Report submission (collecting data process) Implemented DMS characterization: o System identification used DMS software o Signal detection methods - methods used to identify drug-event associations that should be signalized for further investigation o Data presentation - way the results of the previous phase are displayed in order to facilitate its analysis for risk assessment by researchers or technicians. 210 papers screened 112 papers excluded based on title 98 papers identified for abstracts review 57 papers excluded based on abstract review 41 papers identified for full paper review 27 papers excluded following perusal of full paper 14 papers met inclusion criteria Fig. (1). Identification of published evidence for review.

3 172 Current Drug Safety, 2012, Vol. 7, No. 2 Lima et al. o Public availability Data availability to the scientific community. Finally, a description was performed for each SRS database identified in the review and each DMS identified has having been implemented on the SRS databases. RESULTS This review identified seven SRS databases. Six of them are property of regulatory or drug safety monitoring institutions and one is property of GlaxoSmithKline, a multinational pharmaceutical company. Table 2 presents a summary of their characteristics. UNITES STATES FOOD AND DRUG ADMINISTRAT- ION ADVERSE EVENTS REPORTING SYSTEM The Food and Drug Administration s Adverse Event Reporting System is a large database of voluntarily submitted adverse events, representing the most important resource to the study and identification of adverse reactions to drugs and biological products in the United States [15]. It is in practice since October 1997, when it replaced Spontaneous Reporting System, an electronic database that contained reports since 1969 [15]. Currently contains more than four million reports, considering data from previous Spontaneous Reporting System [18]. Food and Drug Administration s Adverse Event Reporting System receives data from health care professionals, consumers, MAH and other residual sources, such as literature reports. From 1969 to 2002, 60% of the reports received were from health care professionals, 29% from consumers and the remaining from MAH and others [15]. Report submission can be distinguished according to its nature: voluntary, performed essentially by health care professionals and consumers, which occurs through MedWatch, a web-based reporting tool that allows users to fill and send an Online Voluntary Reporting Form; and mandatory, performed by MAH that receive direct reports from health care professionals and consumers and are obliged to further report them to the Food and Drug Administration using a specific formulary sent by mail or by an electronic process [19]. This system s raw reported data are freely available and paid reports of specific drug-event association analysis are available on demand [14]. WORLD HEALTH ORGANIZATION INDIVIDUAL CASE SAFETY REPORTS DATABASE SYSTEM (VIGIBASE) VigiBase is a large and global database of Individual Case Safety Reports, maintained and developed by the Uppsala Monitoring Centre, in Sweden, that conjugates reports from the national centres that integrate the World Health Organization International Drug Monitoring Programme. Currently, there are more than 80 countries submitting their national reports to VigiBase. The top-5 contributors are the United States, the Table 2. Identified SRS Databases Databases Data Origin Report Submission Data Availability Food and Drug Administration s Adverse Event Reporting System United States (consumers, health care professionals and MAH) Paper-based forms MEDWatch (electronic reporting tools) Raw data available Reports available on demand World Health Organization VigiBase World Health Organization Individual Case Safety Reports database - reports sent by national agencies from more than 80 member countries all over the world E2B format reports VigiFlow (web-based reporting tool) ASCII text files (old World Health Organization format) Raw data not available Reports available on demand European Medicines Agency EudraVigilance Data from European Medicines Agency member states (regulatory agencies and MAH) Electronic reports (EVPM-ICSRs) Not available Medicines and Healthcare products Regulatory Agency 's Yellow Card Scheme United Kingdom (consumers, health care professionals and MAH) Paper-based Yellow Cards Electronic Yellow Cards (Electronic reporting tools ) Raw data not available List of all reactions reported for a particular drug Netherlands Pharmacovigilance Centre Lareb Netherlands (consumers, health care professionals and MAH) Paper-based forms Electronic reporting forms Raw data not available List on all reactions reported for a particular drug and all drugs reported associated to a particular reaction Messages about possible signals detected available Guangdong Monitoring Centre Database Spontaneous reports from 21 cities in Guangdong province (China) Web-based reporting tool Not available GlaxoSmithKline Worldwide GlaxoSmithKline 's spontaneous reports No information available Not available Abbreviations: MAH, Market Authorization Holders; EVPM, EudraVigilance PostAuthorization Module; ICSR, Individual Case Safety Report.

4 Adverse Drug Reactions Spontaneous Reporting Systems Current Drug Safety, 2012, Vol. 7, No United Kingdom, Germany, Australia and Canada [6, 7, 16]. The system holds more than six million Individual Case Safety Reports [17]. The submission of Individual Case Safety Reports occurs in three different ways [8]: International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use E2B format document Web-based reporting tool VigiFlow ASCII files in the old World Health Organization format (subset of the E2B format) Vigibase s raw data was not identified as being available, but paid overview or detailed reports are available on demand [18]. EUROPEAN MEDICINES AGENCY EUDRAVIGILANCE (EUDRAVIGILANCE) EudraVigilance is a central computer database created in 2001, containing ADR reports for drugs licenced in the European Union. Reports are submitted electronically by member states regulatory agencies and pharmaceutical companies [22, 23], in compliance to the EudraVigilance Post- Authorization Module, designed for post-authorization Individual Case Safety Reports [22]. No information was found on the availability of EudraVigilance s data. MEDICINES AND HEALTHCARE PRODUCTS REGULATORY AGENCY YELLOW CARD SCHEME Spontaneous reports have been collected in the United Kingdom since 1964 through the Yellow Card Scheme, developed and maintained by Medicines and Healthcare products Regulatory Agency 's (that replaced, in 2003, the Medical Devices Agency and the Medicines Control Agency), an agency of the Department of Health that operates postmarketing drug safety surveillance systems in the United Kingdom. Its database contains more than half a million reports of suspected ADRs voluntarily submitted by healthcare professionals and patients by three available means [24, 25]. Online Yellow Card Report; Yellow Card Form, available at pharmacies and General Practitioner surgeries or downloadable; Yellow Card hotline. Medicines and Healthcare products Regulatory Agency's Yellow Card Scheme s raw data is not available, but its website provides lists of all suspected ADRs reported [25]. NETHERLANDS PHARMACOVIGILANCE CENTRE LAREB (LAREB) Lareb, that stands for Landelijke Registratie en Evaluatie van Bijwerkingen (National Registration and Evaluation of Adverse drug reactions) and goes by the name of Netherlands Pharmacovigilance Centre Lareb since 2002, was founded in 1991 with the purpose of maintaining the Netherlands national Spontaneous Reporting System (SRS) on behalf of the Dutch Medicines Evaluation Board [6]. It collects ADRs reported by health care professionals, patients and MAH through paper forms or online reporting forms [26]. Lareb s raw data are not made available outside the Netherlands Pharmacovigilance Centre, but there is a large variety of information that can be accessed on its website [26], such as: List of all reactions reported for a particular drug; List of all drugs reported associated to a particular reaction; Messages concerning the detection of possible signals. GUANGDONG MONITORING CENTRE DATABASE Guangdong Monitoring Centre Database of ADRs contains reports from a population of over 82 million people from the 21 existing cities in the Chinese province of Guangdong. In 2007, it contained over 40 thousand qualified reports, and, in 2009, about three to five thousand new reports were added quarterly [9]. No information could be found regarding the availability of Guangdong Monitoring Centre Database s data. GLAXOSMITHKLINE S SPONTANEOUS REPORTS DATABASE GlaxoSmithKline s database contains spontaneously reported suspected ADRs and supports the pharmacovigilance of the MAH s marketed products. Until 2007, it typically received more than 70 thousand spontaneous reports a year [10]. No information was found regarding the way reports are submitted and which entities are meant to do so, as well as whether GlaxoSmithKline s internal data is available. Five different DSMs have been implemented in the previously presented SRS databases. In Table 3 each implementation s characteristics are summarized. ORACLE HEALTH SCIENCES EMPIRICA SIGNAL (EMPIRICA) Empirica is the latest version of WebVDME, providing a visual data mining environment for detecting signals, uncovering patterns and recognizing emerging trends in spontaneous adverse event report data. The resulting scores can be reviewed in tabular or graphical format [27]. Food and Drug Administration s Adverse Event Reporting System, GlaxoSmithKline and Medicines and Healthcare products Regulatory Agency's Yellow Card Scheme use Empirica. The first two implemented it with Multi-item Gamma Poisson Shrinker and its correspondent measure of disproportionality, Empirical Bayes Geometric Mean [4], while the last one implemented it with Proportional Reporting Ratios [14]. These implementations are only available inside each institution.

5 174 Current Drug Safety, 2012, Vol. 7, No. 2 Lima et al. Table 3. DSM Implementation on Identified SRS Databases DMS SRS Database Used Data Mining Techniques/Measures of Disproportionality Data Presentation System Implementation Availability Food and Drug Administration s Adverse Event Reporting System EBGM Oracle Health Sciences Empirica Signal software Medicines and Healthcare products Regulatory Agency 's Yellow Card Scheme PRR Tabular and graphic form resulting scores GlaxoSmithKline EBGM Bayesian Confidence Propagation Neural Network software World Health Organization Vigibase IC Interactive visualization module Conditional Lareb s graphical interface EudraVigilance Data Analysis System + EudraVigilance Analysis Toolkit Netherlands Pharmacovigilance Centre Lareb European Medicines Agency EudraVigilance ROR No information available PRR Graphical interface Guandong Quantitative Signal Detection System Guangdong Monitoring Centre Database IC Tabular and graphic form resulting scores Abbreviations: EBGM, Empirical Bayes Geometric Mean; IC, Information Component; PRR, Proportional Reporting Ratio; ROR, Reporting Odds Ratio. BAYESIAN CONFIDENCE PROPAGATION NEURAL NETWORK DATA MINING SOFTWARE Bayesian Confidence Propagation Neural Network data mining software was developed by Orre and associates, in cooperation with the World Health Organization Collaborative Centre for International Drug Monitoring, in order to data mine Vigibase for signal detection. It uses Bayesian Confidence Propagation Neural Network s Information Component as its measure of disproportionality and has a module for interactive visualization of query results that produces automatic presentation of results in graphical and table forms [17, 28]. This software was identified as being accessible to health professionals through a paid access to a web-based module. LAREB S GRAPHICAL INTERFACE Lareb s graphical interface was an in-house development meant to facilitate a fast and efficient screening of its database for possible drug-drug interactions or drug-related syndromes using Reporting Odds Ratio as the measure of disproportionality [26]. No information was found regarding the system s availability and the way it presents data for analysis. EUDRAVIGILANCE DATA ANALYSIS SYSTEM + EUDRAVIGILANCE ANALYSIS TOOLKIT EudraVigilance Data Analysis System is an in-house implementation, designed to allow users to analyse safety data collected in EudraVigilance in order to understand the safety profile of medicinal products. It provides a range of analytical tools for measuring reporting compliance for regulatory purposes and signal detection, using Proportional Reporting Ratios as the measure of disproportionality. EudraVigilance Analysis Toolkit, also an in-house implementation, provides predefined table and graph formats for report presentation, having many available functions for the manipulation of report results. No information was found regarding both modules availability [23]. GUANGDONG QUANTITATIVE SIGNAL DETECT- ION SYSTEM Guandong Quantitative Signal Detection System, a webbased quantitative signal detection system for ADRs, comprises three software modules that prepare data, detect associations, and generate reports. This in-house development was implemented over Guangdong Monitoring Centre Database and was based on the Guangdong ADR monitoring platform. It defined Information Component analysis as the system s measure of disproportionality [9]. No information was found on the way results are presented to users, as well as on the availability of the system. DISCUSSION A thorough and systematized analysis of SRS databases outside the institutions responsible for them depends on the availability of these systems raw data, and the consequent possibility of analysing it by applying selected data mining and signal detection methods, and of DMSs, to accomplish data analysis with selected data mining and signal detection methods. This review identified several obstacles on both aspects. Only Food and Drug Administration s Adverse Event

6 Adverse Drug Reactions Spontaneous Reporting Systems Current Drug Safety, 2012, Vol. 7, No Reporting System s raw data are available, allowing its interpretation by selected data mining and signal detection methods to take place. World Health Organization allows paid access to a web-based data search module [17, 28], and Medicines and Healthcare products Regulatory and Lareb provide it for free [25,26], but the available data cannot be manipulated and therefore no data mining and signal detection method can be applied. GlaxoSmithKline s database and Guangdong Monitoring Centre Database data were not identified as being available. Regarding the analysed DMS implementations, none was identified as being available, except for Bayesian Confidence Propagation Neural Network software over Vigibase, accessible by health professionals through a paid access to a web-based module [17, 28]. CONCLUSION Considering what has been exposed, it comes as a conclusion that SRS databases and DMSs are freely available to researchers within institutions responsible for maintaining the systems, but not to most researchers within the scientific community. This hinders the possibility of performing adequate scientific studies on drug safety. Available databases and DMSs should allow the spreading of researchers participating in the process of discovering previously unknown drug safety related problems, creating additional knowledge from the available information or presenting it from other points of view. This review presents some limitations. It is to be expected that most SRS databases have an implemented DMS. It is also possible that other agencies have freely available raw data. None of these issues were identified through our research methodology. ACKNOWLEDGEMENT Declared none. CONFLICT OF INTEREST Declared none. REFERENCES [1] Lu Z, Information technology in pharmacovigilance: Benefits, challenges, and future directions from industry perspectives. Drug Healthc Patient Saf 2009: 1: [2] Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54: [3] EP van Puijenbroek AB, Leufkens HGM, Lindquist M, Orre M, Egberts ACG. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf 2002; 11(1): [4] Szarfman A, Machado SG, O'Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-thanexpected combinations of drugs and events in the US FDA s spontaneous reports database. Drug Saf 2002; 25(6): [5] Wilson AM, Thabane L, Holbrook A. Application of data mining techniques in pharmacovigilance. Br J Clin Pharmacol; (2): [6] van Puijenbroek E, Diemont W, van Grootheest K. Application of quantitative signal detection in the Dutch spontaneous reporting system for adverse drug reactions. Drug Saf 2003; 26 (5): [7] Bate A. Bayesian confidence propagation neural network. Drug Saf 2007; 30(7): [8] Lindquist M. VigiBase, the WHO Global ICSR Database System: Basic Facts. Drug Information J 2008; 42: [9] Li C, Xia J, Deng J, et al. A web-based quantitative signal detection system on adverse drug reaction in China. Eur J Clin Pharmacol 2009; 65: [10] Almenoff J. Innovations for the future of pharmacovigilance. Drug Saf 2007; 30(7): [11] Hauben M, Bate A. Decision support methods for the detection of adverse events in post-marketing data. Drug Discov Today 2009; 14(7/8) [12] Bate A, Lindquist M, Edwards IR, Orre R. A data mining approach for signal detection and analysis. Drug Saf 2002; 25(6): [13] Almenoff JS, Pattishall EN, Gibbs TG, DuMouchel W, Evans SJ, Yuen N. Novel statistical tools for monitoring the safety of marketed drugs. Clin Pharmacol Ther 2007; 82(2): [14] Evans SJ, Waller PC, Davis S. Use proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf 2001; 10: [15] Diane K, Wysowski LS. Adverse drug event surveillance and drug withdrawals in the United States, Arch Intern Med 2005; 165: [16] Almenoff J, Tonning JM, Gould AL. Perspectives on the use of data mining in pharmacovigilance. Drug Saf 2005; 28(11): [17] Orre R. On Data mining and classification using a bayesian confidence propagation neural network, in Department of Numerical Analysis and Computer Science. Royal Institute of Technology: Stockholm, Sweden [18] FDA. Adverse Event Reporting System (AERS) [accessed 12/9/2011]; Available from: ecomplianceregulatoryinformation/surveillance/adversedrugeffe cts/default.htm. [19] FDA. MedWatch: The FDA Safety Information and Adverse Event Reporting Program [accessed 12/9/2011]; Available from: [20] Sten Olsson GB. UR 54. Uppsala Reports 2011; ISSN [21] UMC. The WHO Global ICSR database [accessed 12/9/2011]; Available from: 247&mn1=7347&mn2=7252&mn3=7258. [22] EMA. Guideline on the use of statistical signal detection methods in the Eudravigilance data analysis system [accessed 12/9/2011]; Available from: _GB/document_library/Regulatory_and_procedural_guideline/200 9/11/WC pdf. [23] EMA. EudraVigilance 2011 [accessed 12/9/2011]; Available from: [24] MHRA. Guidance Notes and Application Form for Access to Yellow Card and Adverse Drug Reactions On line Information Tracking (ADROIT) Data [accessed 12/9/2011]; Available from: [25] MHRA. Yellow Card Scheme /9/2011]; Available from: [26] Lareb. Netherlands Pharmacovigilance Centre Lareb [accessed 12/9/2011]; Available from: [27] Phase-Forward. Post-marketing surveillance [accessed 12/9/2011]; Available from: cts/safety/stratpharma [28] World Health Organization. UMC, Viewpoint Part 2, watching for safer medicines. The Uppsala Monitoring Centre Received: September 22, 2011 Revised: May 2, 2012 Accepted: May 17, 2012

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