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A European perspective to networking in drug safety, EU-ADR as example

A European perspective to networking in drug safety, EU-ADR as example. Prof. Miriam CJM Sturkenboom and Ir. Martijn Schuemie on behalf of the EU-ADR consortium. Started february 2008.

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A European perspective to networking in drug safety, EU-ADR as example

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  1. A European perspective to networking in drug safety, EU-ADR as example Prof. Miriam CJM Sturkenboom and Ir. Martijn Schuemie on behalf of the EU-ADR consortium

  2. Started february 2008 EU-ADR IS A RESEARCH AND DEVELOPMENT PROJECT FUNDED BY THE INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) AREA OF THE EUROPEAN COMMISSION UNDER THE VII FRAMEWORK PROGRAMME

  3. Consortium AZ AUH-AS UNOTT Erasmus MC ICL LSHTM PHARMO USC UB2 UNIMIB PEDIANET UAVR SIMG ARS FIMIM UPF NEUROLESI TAU (Tel–Aviv)

  4. The overall objective of the ALERT project is the design, development and validation of a computerized system that exploits data from electronic healthcare records and biomedical databases for the early detection of adverse drug reactions. Supposed to be complementary to existing systems, have more power and detect signals earlier

  5. EU-ADR concept Medical databases: Now 30 Million persons (IT, NL, UK, DK) Mapping of events and drugs Data extraction: periodic Development of extraction tools Data mining Signal generation Literature Known side effects Signal substantiation Pathway analysis Retrospective and prospective signal validation In-silico simulation www.euadr-project.org

  6. Electronic medical IPCI (NL) QRESEARCH (UK) PEDIANET (IT) HSD (IT) Administrative PHARMO (NL) Aarhus (DK) ARS: Tuscany (IT) UNIMIB: Lombardy (IT) Type of databases currently involved Open to other databases

  7. Important choices • Only selected number of events to be studied (events that are relevant in terms of drug safety) • Data remain local (distributed network approach) and only query results will be shared

  8. Trifiro et al. Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor? Pharmacoepidemiol Drug Saf. 2009 Sep 15

  9. Linking databases and data extraction for signal generation in EU-ADR Extraction of data • Step 1: Mapping of codes (disease, drugs, language): UMLS • Step 2: Application of Jerboa to elaborate and extract aggregated data locally (distributed network approach) Quality control • Step 3: Comparison of rates • Step 4: Inspection of drug utilization • Step 5: Assessment of drug-event associations (validation sets: true positive / true negative) Avillach P. et al. A semantic approach for the homogeneous identification of events in eight patient databases: a contribution to the European eu-ADR project. Stud Health Technol Inform. 2009;150:190-4.

  10. Step 1: Terminology mappingHow to deal with different codes between DBs? Resource: the UMLS • Unified Medical Language System • Metathesaurus • include more than 150 source vocabularies • ~ 1,6 million concepts (sets of synonyms) • Semantic Network • Lexical Resources From: Paul Avillach, Frantz Thiessard, Bordeaux www.euadr-project.org

  11. Workflow of Terminology mapping 3. Search for UMLS concepts Event 2. Event Description Form www.euadr-project.org From: Paul Avillach, Frantz Thiessard, Bordeaux

  12. 4. First Draft of Terminology mapping 3. Search for UMLS concepts 5. Conference call to discuss harmonisation issues From: Paul Avillach, Frantz Thiessard, Bordeaux […]

  13. Step 2: Data extraction • Distributed network approach for obtaining the data • No analytical capabilities or common software in all sites • Software developed in JAVA on purpose:

  14. Database 1 Database 2 Database .. n Step 2: Software for extraction of data LOCAL Input Input Input … … Output Output Output Aggregated data Script “pooling” SHARED www.euadr-project.org

  15. Current status data availability Aggregated data available on 20 million subjects, all databases, extensions and updates ongoing

  16. EU-ADR concept Medical databases: Now 30 Million persons (IT, NL, UK, DK) Mapping of events and drugs Data extraction: periodic Development of extraction tools Data mining Signal generation Literature Known side effects Signal substantiation Pathway analysis Retrospective and prospective signal validation In-silico simulation www.euadr-project.org

  17. Signal substantiation (automated)

  18. Signal substantiation (automated) Drug / Event Yes (annotate) Known side effect from DBs Signal list No • Mining of already described drug-event relationships from: • Regulatory databases: • SPC • literature: • Text mining on Medline records • Pharmacological databases: • Expert curated databases • Requires text mining as most information is in plain text

  19. Signal substantiation (automated) Drug Target of drug list D-T • The following information is obtained for a drug: • -List of metabolites • -List of targets • Is a combination of the following • List of targets for the drug and its metabolites obtained by in silico profiling • List of targets for the drug and its metabolites obtained by text mining of literature and specialized databases

  20. Signal substantiation (automated) Event Target to event list E-T The following information is obtained for a event: -target-event relationships by text mining of the literature and/or specialized databases -Sequence variants of the targets that might be associated with the phenotype (event) are considered

  21. Signal substantiation (automated) Target of drug list Map targets to network Target to event list Path that connects drug to event through biological reactions

  22. Signal substantiation (automated) signal substantiation pipeline Signal Substantiation score for the signal

  23. Conclusion • EU-ADR is aiming to develop a system to complement existing pharmacovigilance tools. • EU-ADR is methodologically oriented • Jerboa already used by many other projects (vaccine safety, NSAID safety) • EU-ADR is open: other databases are welcome to join

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