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Discussion of Dynamic Enhancement of Drug Product Labels at Data Integration in the Life Sciences 2012


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Discussion of Dynamic Enhancement of Drug Product Labels at Data Integration in the Life Sciences 2012

  1. 1. Dynamic Enhancement of Drug Product Labels Through Semantic Web Technologies (A W3C HCLS IG Use Case)Richard Boyce, University of PittsburghOktie Hassanzadeh, IBM ResearchJyotishman Pathak, Mayo ClinicJodi Schneider, DERI, GalwayMaria Liakata, Aberystwyh University/EMBL-EBIAnita de Waard, Elsevier 1 Biomedical Informatics Department of Biomedical Informatics
  2. 2. The problem• Drug product labels are a key source of information – Drug efficacy, effectiveness, safety, use• However, much of the information in drug product labeling is incomplete or not up to date with the scientific literature 2 Biomedical Informatics
  3. 3. Missing drug-drug interactions• Example: – “Both clopidogrel and ticlopidine significantly inhibited the CYP2B6-catalyzed bupropion hydroxylation. Patients receiving either clopidogrel or ticlopidine are likely to require dose adjustments when treated with drugs primarily metabolized by CYP2B6.” [1]• Search bupropion product labels for “clopidogrel” – No mention at all in Aplenzin ER insert [2] – Mention in the generic tablet insert [3], but refers only to hypothetical interaction1. Turpeinen M, Tolonen A, Uusitalo J, Jalonen J, Pelkonen O, Laine K. Effect of clopidogrel and ticlopidine on cytochrome P450 2B6 activity as measured bybupropion hydroxylation. Clin Pharmacol Ther. 2005 Jun;77(6):553-9.2. 3 Biomedical Informatics
  4. 4. Other notable incomplete information • Clinical effectiveness – Ex: Switch to venlafaxine in adolescents: • “A switch to another SSRI was just as efficacious as a switch to venlafaxine and resulted in fewer adverse effects.” [1] • Age-related clearance data – Product labels rarely have this information even when published [2] • Metabolic inhibition – Numerous examples of missing information [3] 1. Brent D, Emslie G, Clarke G, Wagner KD, Asarnow JR, Keller M, Vitiello B, Ritz L, Iyengar S, Abebe K, irmaher B, Ryan N, Kennard B, Hughes C, DeBar L, McCracken J, Strober M, Suddath R, Spirito A, Leonard H, Melhem N, Porta G, Onorato M, Zelazny J. Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. JAMA. 2008 Feb 27;299(8):901-13. 2. Boyce RD, Handler SM, Karp JF, Hanlon JT. Age-Related Changes in Antidepressant Pharmacokinetics and Potential Drug-Drug Interactions: A Comparison of Evidence-Based Literature and Package Insert Information. Am J Geriatr Pharmacother. 2012 Apr;10(2):139-50 3. Boyce R, Harkema H, Conway M. Leveraging the semantic web and natural language processing to enhance drug-mechanism knowledge in drug product labels. In: Proceedings of the 1st ACM International Health Informatics Symposium. IHI ’10. New York, NY, USA: ACM; 2010:492–496. 4 Biomedical Informatics
  5. 5. Current use of product labels
  6. 6. What are we suggesting then?• “…build a linked open data store of scientific information that updates or elaborates on medication safety statements present in drug product labels.” - W3C HCLS IG Use Case ( 6 Biomedical Informatics
  7. 7. Proposed innovation – take claims present insemantic web resources….
  8. 8. …create a linked dataset that merges those claimsand identifies where they should be located in theproduct label….
  9. 9. …create customized views of the new linked datasettailored toward various drug experts and decisionsupport tools
  10. 10. Proof of concept• – 29 psychotropic drug products – Created using four Semantic Web nodes 1. 2. 3. 4. 10 Biomedical Informatics
  11. 11. Preliminary resultsDrug DDIs apparently novel to the Drug relevant to the Interactions section Clinical Studies NDF-RT DIKB section Critical SignificantAntidepressantsBupropion 5 2 4 0Citalopram 4 8 20 1Venlafaxine 2 6 21 3…AntipsychoticsRisperidone 23 0 10 3Ziprasidone 1 23 55 0…Sedative HypnoticsEszopiclone 3 0 2 0… 11 Biomedical Informatics
  12. 12. Issues to be addressed• Ensuring the quality of the claims linked to the package insert sections – Provenance is key – Could a drug information “knowledge market” could be created?• Technical issues with some existing linked open drug data nodes – Now is the time to improve linked open drug data • Correct RDF mappings, accurate encodings • Graph and data provenance 12 Biomedical Informatics
  13. 13. Want more information?• Use Case description –• Google code project –• Proof of concept –• Linked data nodes used in the proof of concept – – – 13 Biomedical Informatics
  14. 14. Discussion/questions 14 Biomedical Informatics
  15. 15. Acknowledgements• The Drug Interaction Knowledge Base team – John Horn Pharm.D, Carol Collins MD, Greg Gardner, Rob Guzman• W3C LODD Task Force• Early comments on the Use Case: – Michel Dumontier and several others who attend W3C HCLS calls 15 Biomedical Informatics