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Dynamic Enhancement
 of Drug Product Labels
 Through Semantic Web
 Technologies
 (A W3C HCLS IG Use Case)
Richard Boyce, University of Pittsburgh
Oktie Hassanzadeh, IBM Research
Jyotishman Pathak, Mayo Clinic
Jodi Schneider, DERI, Galway
Maria Liakata, Aberystwyh University/EMBL-EBI
Anita de Waard, Elsevier


                                        1       Biomedical Informatics
 Department of Biomedical Informatics
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
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 interaction
1. Turpeinen M, Tolonen A, Uusitalo J, Jalonen J, Pelkonen O, Laine K. Effect of clopidogrel and ticlopidine on cytochrome P450 2B6 activity as measured by
bupropion hydroxylation. Clin Pharmacol Ther. 2005 Jun;77(6):553-9.
2. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=2315d6e5-144d-4163-9711-f855c759cf8e
3. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=e537ed50-6677-45eb-9e62-af62d3831f19



                                                                              3                                   Biomedical Informatics
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
Current use of product labels
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 (http://goo.gl/wP4Mz)




                             6            Biomedical Informatics
Proposed innovation – take claims present in
semantic web resources….
…create a linked dataset that merges those claims
and identifies where they should be located in the
product label….
…create customized views of the new linked dataset
tailored toward various drug experts and decision
support tools
Proof of concept
• http://tinyurl.com/c53skm4
   – 29 psychotropic drug products
   – Created using four Semantic Web nodes




       1.   http://thedatahub.org/dataset/linked-structured-product-labels
       2.   http://thedatahub.org/dataset/linkedct
       3.   http://bioportal.bioontology.org/ontologies/47101
       4.   http://thedatahub.org/dataset/the-drug-interaction-knowledge-base


                                                          10                    Biomedical Informatics
Preliminary results
Drug                 ClinicalTrials.gov        DDIs apparently novel to the Drug
                     relevant to the           Interactions section
                     Clinical Studies                     NDF-RT                DIKB
                     section                   Critical       Significant
Antidepressants
Bupropion                       5                    2              4            0
Citalopram                      4                    8             20            1
Venlafaxine                     2                    6             21            3
…
Antipsychotics
Risperidone                    23                   0              10            3
Ziprasidone                     1                   23             55            0
…
Sedative Hypnotics

Eszopiclone                     3                    0              2            0
…


                                          11                    Biomedical Informatics
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
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://tinyurl.com/c53skm4
• 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



                                     13              Biomedical Informatics
Discussion/questions




                14     Biomedical Informatics
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

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

  • 1. Dynamic Enhancement of Drug Product Labels Through Semantic Web Technologies (A W3C HCLS IG Use Case) Richard Boyce, University of Pittsburgh Oktie Hassanzadeh, IBM Research Jyotishman Pathak, Mayo Clinic Jodi Schneider, DERI, Galway Maria Liakata, Aberystwyh University/EMBL-EBI Anita de Waard, Elsevier 1 Biomedical Informatics Department of Biomedical Informatics
  • 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. 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 interaction 1. Turpeinen M, Tolonen A, Uusitalo J, Jalonen J, Pelkonen O, Laine K. Effect of clopidogrel and ticlopidine on cytochrome P450 2B6 activity as measured by bupropion hydroxylation. Clin Pharmacol Ther. 2005 Jun;77(6):553-9. 2. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=2315d6e5-144d-4163-9711-f855c759cf8e 3. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=e537ed50-6677-45eb-9e62-af62d3831f19 3 Biomedical Informatics
  • 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. Current use of product labels
  • 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 (http://goo.gl/wP4Mz) 6 Biomedical Informatics
  • 7. Proposed innovation – take claims present in semantic web resources….
  • 8. …create a linked dataset that merges those claims and identifies where they should be located in the product label….
  • 9. …create customized views of the new linked dataset tailored toward various drug experts and decision support tools
  • 10. Proof of concept • http://tinyurl.com/c53skm4 – 29 psychotropic drug products – Created using four Semantic Web nodes 1. http://thedatahub.org/dataset/linked-structured-product-labels 2. http://thedatahub.org/dataset/linkedct 3. http://bioportal.bioontology.org/ontologies/47101 4. http://thedatahub.org/dataset/the-drug-interaction-knowledge-base 10 Biomedical Informatics
  • 11. Preliminary results Drug ClinicalTrials.gov DDIs apparently novel to the Drug relevant to the Interactions section Clinical Studies NDF-RT DIKB section Critical Significant Antidepressants Bupropion 5 2 4 0 Citalopram 4 8 20 1 Venlafaxine 2 6 21 3 … Antipsychotics Risperidone 23 0 10 3 Ziprasidone 1 23 55 0 … Sedative Hypnotics Eszopiclone 3 0 2 0 … 11 Biomedical Informatics
  • 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. 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://tinyurl.com/c53skm4 • 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 13 Biomedical Informatics
  • 14. Discussion/questions 14 Biomedical Informatics
  • 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