Boyce cshals-2012-linked-sp ls-final

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This presentation describes several deficiencies of drug product labels and how information on the semantic web can be used to update the drug product label.

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Boyce cshals-2012-linked-sp ls-final

  1. 1. Dynamic Enhancement of Drug Product Labels Through Semantic Web Technologies (A W3C HCLS IG Use Case) Richard Boyce, University of Pittsburgh Maria Liakata, Aberystwyh University/EBI Jodi Schneider, DERI, Galway Anita de Waard, Elsevier 1 Biomedical InformaticsDepartment of Biomedical Informatics
  2. 2. Overview• The motivation for the W3C Use Case• What we are suggesting• How it can be accomplished – Proof of concept• Issues to be addressed• Discussion 2 Biomedical Informatics
  3. 3. The problem• Much of the information in drug package inserts (aka product labels) is incomplete or has not been updated 3 Biomedical Informatics
  4. 4. 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 drug package inserts 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. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=2315d6e5-144d-4163-9711-f855c759cf8e3. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=e537ed50-6677-45eb-9e62-af62d3831f19 4 Biomedical Informatics
  5. 5. Missing metabolic properties • Cimetidine inhibition of CYP3A4 and CYP1A2 metabolism – Noted in an FDA draft guidance [1] but not PI for the generic tablet [2] • Fluconazole inhibition of CYP3A4 metabolism – Noted in an FDA draft guidance [1] but not directly in the fluconazole solution PI [3] • Inferable from the stated interaction with midazolam • See others like this in [4]1. FDA. FDA Guideline: Drug Interaction Studies – Study Design, Data Analysis, and Implications for Dosing and Labeling. Rockville, MD: Food and Drug Administration; 2006. Available at: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072101.pdf.2. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=3a2f773d-b67f-4e83-b8d7-fce6b2887efe3. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=26672a97-adf3-9f1d-4bdc-b2606747bbf54. 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. Available at: http://doi.acm.org/10.1145/1882992.1883070. 5 Biomedical Informatics
  6. 6. Missing age-related clearance data • The PI for citalopram [1] notes age-related pharmacokinetic changes – “…subjects ≥ 60 years of age were compared to younger subjects in two normal volunteer studies. In a single-dose study, citalopram AUC and half-life were increased in the elderly subjects by 30% and 50%, respectively” • However, clearance (Cl) would be the preferred measure of age-related change – is there literature on this? – Yes! [2-4] • The package inserts rarely have this information even when published [5]1. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=4259d9b1-de34-43a4-85a8-41dd214e91772. Reis M, Lundmark J, Bengtsson F. Therapeutic drug monitoring of racemic citalopram: a 5-year experience in Sweden, 1992-1997. Ther Drug Monit. 2003;25(2):183- 191.3. Gutierrez M, Abramowitz W. Steady-state pharmacokinetics of citalopram in young and elderly subjects. Pharmacotherapy. 2000;20(12):1441-1447.4. Bies RR, Feng Y, Lotrich FE, et al. Utility of sparse concentration sampling for citalopram in elderly clinical trial subjects. J Clin Pharmacol. 2004;44(12):1352-1359.5. 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 Jan 27. [Epub ahead of print] PubMed PMID: 22285509. 6 Biomedical Informatics
  7. 7. Is there any requirement that the inserts be kept up to date?• Yes - 2006 regulations explicitly require that studies of drug-drug interactions, metabolic pathways, and special populations be discussed [1]1. FDA. Code of Federal Regulations 21 Part 201.56, chapter Requirements on content and format of labeling for human prescription drug and biological products. Washington, DC: US Government Printing Office, 2010. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=201.57 7 Biomedical Informatics
  8. 8. Why are package inserts important? • Package inserts are primarily intended to be a reference for prescribing clinicians [1]1. Marroum PJ, Gobburu J. The product label: how pharmacokinetics and pharmacodynamics reach the prescriber. Clin Pharmacokinet. 2002;41(3):161-169.2. Ko, Y. et al. Prescribers’ knowledge of and sources of information for potential drug- drug interactions: a postal survey of US prescribers. Drug Saf. 31, 525-536 (2008). 8 Biomedical Informatics
  9. 9. Why is the package insert apparentlyso influential?• Authoritative• Simple (for a drug expert) to follow• Often the only source of information besides FDA approval documentation for new drugs• No standard for searching the scientific literature• No standard for judging the quality of published studies 9 Biomedical Informatics
  10. 10. What are we suggesting then?• “Use natural language processing and scientific discourse ontologies to 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 (http://goo.gl/wP4Mz) 10 Biomedical Informatics
  11. 11. Current use of package inserts
  12. 12. Proposed innovation – take claims present insemantic web resources….
  13. 13. …create a linked dataset that merges those claimsand identifies where they should be located in thepackage insert….
  14. 14. …create customized views of the new linked datasettailored toward various drug experts and decisionsupport tools
  15. 15. The big picture…
  16. 16. How can this be accomplished? 16 Biomedical Informatics
  17. 17. The Structured Product Label (SPL) standard makes this possible• All package inserts for currently marketed drugs are available in this format [1-3] 1. http://www.fda.gov/OHRMS/DOCKETS/98fr/FDA-2005-N-0464-gdl.pdf 2. http://www.fda.gov/ForIndustry/DataStandards/StructuredProductLabeling/default.htm 3. http://dailymed.nlm.nih.gov/dailymed/downloadLabels.cfm 17 Biomedical Informatics
  18. 18. FDA law dictates the kinds of claimsthat should be present in each section 18 Biomedical Informatics
  19. 19. Many of these claims can be identified inresources on the Semantic Web….Drug Interaction Knowledge Base : http://thedatahub.org/dataset/linked-structured-product-labelsClinicalTrials.gov: http://thedatahub.org/dataset/linkedctDrugBank: http://thedatahub.org/dataset/the-drug-interaction-knowledge-base 19 Biomedical Informatics
  20. 20. …and then linked to package insertsections published on the Semantic Web Linked SPLs: http://thedatahub.org/dataset/linked-structured-product-labels 20 Biomedical Informatics
  21. 21. Proof of concept 21 Biomedical Informatics
  22. 22. Overview of the proof of concept• http://goo.gl/rs3Fy – Package inserts for drug products containing citalopram and venlafaxine – Created using three Semantic Web nodes • http://thedatahub.org/dataset/linked-structured-product-labels • http://thedatahub.org/dataset/linkedct • http://thedatahub.org/dataset/the-drug-interaction-knowledge-base 22 Biomedical Informatics
  23. 23. More about the data nodes• http://goo.gl/rs3Fy 1. http://thedatahub.org/dataset/linked-structured-product-labels 2. http://thedatahub.org/dataset/linkedct 3. http://thedatahub.org/dataset/the-drug-interaction-knowledge-base 23 Biomedical Informatics
  24. 24. Scientific discourse in the “DIKB”• http://goo.gl/rs3Fy 1. http://thedatahub.org/dataset/linked-structured-product-labels * Expert-curated 2. http://thedatahub.org/dataset/linkedct 3. http://thedatahub.org/dataset/the-drug-interaction-knowledge-base * Uses SWANCO 24 Biomedical Informatics
  25. 25. Take the drug interactions section of adrug package insert… 25 Biomedical Informatics
  26. 26. Make it simple for the package insertreader to see claims that could expand orupdate the information in this section… 26 Biomedical Informatics
  27. 27. Example: an interaction affecting venlafaxine that maynot be in this section… 27 Biomedical Informatics
  28. 28. Example: an interaction affecting venlafaxine that maynot be in this section…NLP determined that evidence for adiphenhydramine/venlafaxine interaction is notmentioned 28 Biomedical Informatics
  29. 29. Clicking on the link provides a path toevidence for (or against) the drug interactionclaim…
  30. 30. One goal is to link directly to statements and sections within scientific documents from which evidence was derived• For example, assume a drug interaction claim – identify core scientific concepts (CoreSC) in the paper containing the evidence for/against the claim (background, methods, results,…) – Identify the text containing evidence for/against a claim within the CoreSC – Link the specific CoreSC element back to the package insert section (via the interaction claim)• Currently testing this approach using SAPIENTA (www.sapientaproject.com)
  31. 31. Just a couple of the issues to beaddressed• Ensuring the quality of the claims linked to the package insert sections – Provenance is key – Would nanopublications help here? • The sky could be the limit if 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 31 Biomedical Informatics
  32. 32. Want more information?• Use Case description – http://goo.gl/wP4Mz• Google code project – code.google.com/p/swat-4-med-safety/• Proof of concept – http://goo.gl/rs3Fy• Linked data nodes used in the proof of concept – http://thedatahub.org/dataset/linked-structured-product-labels – http://thedatahub.org/dataset/the-drug-interaction-knowledge-base – http://thedatahub.org/dataset/linkedct 32 Biomedical Informatics
  33. 33. 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 33 Biomedical Informatics
  34. 34. Discussion/questions 34 Biomedical Informatics
  35. 35. The package insert’s influence is significant • How frequently do three drug interaction checking tools agree on an interaction by source? (unpublished) – 44 interactions from the package insert or scientific literature (25 newer psychotropics) – Agreement 3.25 times more likely for package insert only interactions then for literature onlyScientific literature Package insert 15.4% 38.5% 50% 35 Biomedical Informatics

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