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Linked Data and SPLs – 
Strengths and Limitations 
1 Biomedical Informatics 
Richard D. Boyce, PhD 
University of Pittsbur...
The drug product label complements 
other knowledge sources… 
• Drug interactions: 
– 40% of 44 pharmacokinetic drug-drug ...
…but there are also some information gaps 
• pharmacokinetic information provided by 
product labels for older drugs [1] 
...
“Take home” point 
• Linking product labeling to other trusted 
sources of information might better meet 
the drug informa...
Claims present in 
Semantic Web resources 
Product labels 
Scientific literature 
Pre-market data 
Post-market data 
Linke...
How it works – step 1 
• Start with SPL documents 
6 Biomedical Informatics
How it works – step 2 
• Convert each part of an SPL document to a 
“triple” 
predicate 
subject object 
• The subject, pr...
How it works – step 3 
• Use identifiers (URIs) that are used by other 
relevant sources 
– Must use common URIs or to ena...
How it works – step 4 
• Make the new “Linked SPLs” data source accessible 
on the Internet to enable cross-resource queri...
Proof of concept - overview 
• Claims from 3 drug information sources 
were linked to the labels for drug products 
that c...
Proof of concept – the chosen SPLs 
• 29 active ingredients used in psychotropic 
drug products (i.e., antipsychotics, 
an...
Proof of concept – Clinical Studies 
• For each of the 29 drugs, a Linked Data version of 
ClinicalTrials.gov [1] was quer...
Proof of concept – Clinical Studies mashup
LinkedCT 
02/2000 – 
2/2012 
SPARQL: Get PubMed ID for all 
Retrieved 170 
records from 
PubMed 
“results references” 
for...
Proof of concept – Clinical Studies validation 
15 Biomedical Informatics
Proof of concept – Drug Interactions 
• For each of the 29 drugs, a Linked Data version of the 
VA NDF-RT [1] was queried ...
Proof of concept – Drug Interactions mashup
Proof of concept – Drug Interactions preliminary 
exploration 
• How often the link provide more complete 
18 Biomedical I...
Proof of concept – Drug Interactions preliminary 
exploration cont… 
Filter out DDIs 
previously identified 
in antidepres...
Proof of concept – Drug Interactions preliminary 
exploration cont… 
• At least one potentially novel interaction was link...
Lessons learned 
• A method is needed to deal with multiple study 
arms in ClinicalTrials.gov 
– Study NCT00015548 (The CA...
Lessons learned cont… 
• Potentially novel DDIs are sometimes 
implicit in drug groupings mentioned in 
labeling 
– escita...
LinkedSPLs – A research program 
23 Biomedical Informatics
Growing interest in how to mine 
the unstructured text in SPLs 
• PubMed query for research mentioning 
natural language p...
LinkedSPLs – A research program 
25 Biomedical Informatics 
Annotations 
would go here!
Want more information? 
• LinkedSPLs on GitHub 
– https://github.com/bio2rdf/bio2rdf-scripts/tree/release3/linkedSPLs 
26 ...
Research Team and others who have contributed 
University of Pittsburgh Department of Biomedical Informatics: 
•Harry Hoch...
Acknowledgements 
• Grant funding for the research: 
– National Library of Medicine (R01LM011838-01), The National 
Instit...
Discussion/questions 
29 Biomedical Informatics
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Linked data-and-sp ls-fda-spl-jamboree-092014

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Prof. Boyce discusses the "Linked SPLs" system its relationship to SPLs stored in DailyMed and the OpenFDA initiative. The talk will focus on the potential uses, strengths, and limitations Linked SPLs which represents drug product labeling as Semantic Web Linked Data.

Video of this talk can be found at the link below starting at starts at 3:11:26: http://videocast.nih.gov/summary.asp?Live=14776&bhcp=1

Published in: Health & Medicine
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Linked data-and-sp ls-fda-spl-jamboree-092014

  1. 1. Linked Data and SPLs – Strengths and Limitations 1 Biomedical Informatics Richard D. Boyce, PhD University of Pittsburgh 2014 DailyMed Jamboree Public Workshop September, 18th 2014 Department of Biomedical Informatics
  2. 2. The drug product label complements other knowledge sources… • Drug interactions: – 40% of 44 pharmacokinetic drug-drug interactions affecting 25 drugs were located exclusively in product labeling [1] • Clinical studies: – 24% of clinical efficacy trials for 90 drugs were discussed in the product label but not the scientific literature [2] • Clinical pharmacology: – 1/5th of the evidence for metabolic pathways for 16 drugs and 19 metabolites was found in product labeling but not the scientific literature [3] 1. Boyce RD, Collins C, Clayton M, Kloke J, Horn JR. Inhibitory metabolic drug interactions with newer psycho-tropic drugs: inclusion in package inserts and influences of concurrence in drug interaction screening software. Ann Pharmacother. 2012;46(10):1287–1298. 2. Lee K, Bacchetti P, Sim I. Publication of Clinical Trials Supporting Successful New Drug Applications: A Literature Analysis. PLoS Med. 2008;5(9):e191. 3. Boyce R, Collins C, Horn J, Kalet I. Computing with evidence: Part I: A drug-mechanism evidence taxonomy oriented toward confidence assignment. Journal of Biomedical Informatics. 2009;42(6):979–989.
  3. 3. …but there are also some information gaps • pharmacokinetic information provided by product labels for older drugs [1] • quantitative data on age-related clearance reduction [2] • quantitative data on clearance changes in the elderly [3] • drug-drug interaction information [4] 1. Marroum PJ, Gobburu J: The product label: how pharmacokinetics and pharmacodynamics reach the prescriber. Clin Pharmacokinetics 2002, 41(3):161–169. 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 Geriatric Pharmacother 2012, 10(2):139–150. 3. Steinmetz KL, Coley KC, Pollock BG: Assessment of geriatric information on the drug label for commonly prescribed drugs in older people. J AmGeriatrics Soc 2005, 53(5):891–894. 4. Hines L, Ceron-Cabrera D, Romero K, Anthony M, Woosley R, Armstrong E, Malone D: Evaluation of warfarin drug interaction listings in US product information for warfarin and interacting drugs. Clin Ther 2011, 33:36–45.
  4. 4. “Take home” point • Linking product labeling to other trusted sources of information might better meet the drug information needs of various stakeholders – Clinicians, patients, pharmacovigilance experts, translational researchers • Semantic Web Linked Data can help – Enables information synthesis using web standards on ontologies 4 Biomedical Informatics
  5. 5. Claims present in Semantic Web resources Product labels Scientific literature Pre-market data Post-market data Linked SPLs Customized views Maintainers of tertiary Drug information sources Pharmacists Pharma Drug safety specialist Regulators Decisions support tools Tertiary source Architecture
  6. 6. How it works – step 1 • Start with SPL documents 6 Biomedical Informatics
  7. 7. How it works – step 2 • Convert each part of an SPL document to a “triple” predicate subject object • The subject, predicate, and objects are specified by special identifiers called “URIs” <http://.../setid-XXX> <http://.../activeMoiety> <http://../sertraline> 7 Biomedical Informatics SPL setid XXX sertraline active moiety
  8. 8. How it works – step 3 • Use identifiers (URIs) that are used by other relevant sources – Must use common URIs or to enable links between sources! • SPL <http://.../setid-XXX> <http://.../activeMoiety> <http://../sertraline> • NDF-RT <http://../sertraline> <http://.../potentialDDI> <http://../Ioflupane I-123> Potentially interacts with 8 Biomedical Informatics SPL setid XXX active moiety sertraline Ioflupane I-123
  9. 9. How it works – step 4 • Make the new “Linked SPLs” data source accessible on the Internet to enable cross-resource queries What are the known targets of all active ingredients that are classified as antidepressants? Is there a pharmacogenomics concern for any of the drugs associated with Hyperkalemia Show the evidence support for all pharmacokinetic PDDIs affecting buproprion that are supported by a randomized study 9 Biomedical Informatics
  10. 10. Proof of concept - overview • Claims from 3 drug information sources were linked to the labels for drug products that contain one of 29 psychotropic drugs [1] 1. Boyce RD, Horn JR, Hassanzadeh O, de Waard A, Schneider J, Luciano JS, Rastegar-Mojarad M, Liakata M. Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness. J Biomed Semantics. 2013 Jan 26;4(1):5. 10 Biomedical Informatics
  11. 11. Proof of concept – the chosen SPLs • 29 active ingredients used in psychotropic drug products (i.e., antipsychotics, antidepressants, and sedative/hypnotics) – chosen because they are very widely prescribed and a number of these “newer” psychotropic drugs are involved in drug-drug interactions – 1,102 drug product labels at the time of the study (fall 2012) 11 Biomedical Informatics
  12. 12. Proof of concept – Clinical Studies • For each of the 29 drugs, a Linked Data version of ClinicalTrials.gov [1] was queried for ClinicalTrials.gov studies that were tagged as 1. related to the drug • based on an rdf:seeAlso property to a DrugBank [2] identifier 1. having at least one published result indexed in PubMed • based on a linkedct:trial_results_reference property pointing to a PubMed identifier active moiety SPL setid XXX sertraline see also trial result NLP used to extract 1. http://linkedct.org/ Last Accessed 09/17/2014 2. DrugBank v3.0. http://drugbank.ca/. Last Accessed 09/17/2014 3. SAPIENTA. http://www.sapientaproject.com/software#sapienta_soft . Last Accessed 09/17/2014 DB01104 DrugBank ID Linked CT YYY DB01104 PMID ZZZ conclusions [3]
  13. 13. Proof of concept – Clinical Studies mashup
  14. 14. LinkedCT 02/2000 – 2/2012 SPARQL: Get PubMed ID for all Retrieved 170 records from PubMed “results references” for studies involving a random sample of 9 psychotropic drugs eUtils: Get records for all PubMed IDs Conclusions manually extracted from records • 2 title only records • 2 editorial or letter records “Potential relevance” criteria applied to 166 conclusions 51 potentially relevant conclusions Novelty criteria applied to 39 relevant conclusions • 2 required full text to interpret • 113 judged non-relevant (Kappa = 0.69) • 9 judged non-novel (Kappa = 0.72) 30 relevant and novel conclusions • 12 conclusions apply to off-label use • 11 new population • 25 comparative effectiveness • 5 new treatment method
  15. 15. Proof of concept – Clinical Studies validation 15 Biomedical Informatics
  16. 16. Proof of concept – Drug Interactions • For each of the 29 drugs, a Linked Data version of the VA NDF-RT [1] was queried for drug-drug interactions (DDIs) that were tagged as 1. related to the drug • based on an skos:prefLabel property in Bioportal [2] 1. Indicated as having an “Active” status in the NDF-RT active moiety SPL setid XXX sertraline kind of record NDF-RT YYY interaction active NDF-RT ZZZ record status has participant sertraline preferred label (i.e., name) 1. The NDF-RT is maintained by the Veteran’s Administration. A publicly available version of the resource is present in the Bioportal at http://purl.bioontology.org/ontology/NDFRT 2. http://bioportal.bioontology.org/
  17. 17. Proof of concept – Drug Interactions mashup
  18. 18. Proof of concept – Drug Interactions preliminary exploration • How often the link provide more complete 18 Biomedical Informatics information? VA NDF-RT in Bioportal October 2012 SPARQL: Get all DDIs for antidepressants Filter out DDIs previously identified in antidepressant product labels Tabulate potentially novel PDDIs
  19. 19. Proof of concept – Drug Interactions preliminary exploration cont… Filter out DDIs previously identified in antidepressant product labels Product label DDIs for 20 drugs manually identified [22] • ~70 interactions • Pharmacokinetic and pharmacodynamic 19 Biomedical Informatics We filtered NDF-RT interactions • String matching and an expanded version of the interaction table • ~2,500 drug-drug and drug-class pairs Face validity but future work needed for • validate the accuracy of this approach • create a more scalable approach
  20. 20. Proof of concept – Drug Interactions preliminary exploration cont… • At least one potentially novel interaction was linked to a product label for products containing each of the 20 antidepressants – tranylcypromine (33), nefazodone (31), fluoxetine (28) • Several cases where all of the interactions were potentially novel – e.g., trazodone, venlafaxine, trimipramine 20 Biomedical Informatics • Pharmacist review – Several true positives • e.g., escitalopram-tapentadol, escitalopram-metoclopramide – Some false positives • e.g., nefazodone-digoxin (digitalis)
  21. 21. Lessons learned • A method is needed to deal with multiple study arms in ClinicalTrials.gov – Study NCT00015548 (The CATIE Alzheimer’s Disease Trial) lists four interventions • Three antispychotics and one antidepressant – Led to false positive results for the antidepressant (citalopram) • actually about the effectiveness of an antipsychotic drug – Might be addressable by excluding published results that do not mention an indicated or off-label use of the drug (e.g., “depression” in the 21 Biomedical Informatics case of citalopram)
  22. 22. Lessons learned cont… • Potentially novel DDIs are sometimes implicit in drug groupings mentioned in labeling – escitalopram and tapentadol (NDF-RT) • Implicit in the label as a general statement about additive serotonergic effects • Questions about evidence support for potentially novel data – Several potentially novel NDF-RT interactions that might not be mentioned in the label due to indeterminate evidence. • amoxapine and rifampin 22 Biomedical Informatics
  23. 23. LinkedSPLs – A research program 23 Biomedical Informatics
  24. 24. Growing interest in how to mine the unstructured text in SPLs • PubMed query for research mentioning natural language processing [1]: – 9 MEDLINE abstracts indexed as mentioning product labeling • ~2,800 if “product labeling” removed from the query – The same query two years ago yielded only 2 results! • Main research areas – Pharmacovigilance and decision support 1. Query done on 9/16/14: (Natural Language Processing [MeSH Terms] OR Natural Language Processing [Text Word]) AND ((Drug Labeling [MeSH Terms] OR drug labeling[Text Word]) OR (Product Labeling, Drug [MeSH Terms]) OR ("product labeling" [Text Word])) 24 Biomedical Informatics
  25. 25. LinkedSPLs – A research program 25 Biomedical Informatics Annotations would go here!
  26. 26. Want more information? • LinkedSPLs on GitHub – https://github.com/bio2rdf/bio2rdf-scripts/tree/release3/linkedSPLs 26 Biomedical Informatics • Publications – Boyce RD, Horn JR, Hassanzadeh O, de Waard A, Schneider J, Luciano JS, Rastegar-Mojarad M, Liakata M. Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness. J Biomed Semantics. 2013 Jan 26;4(1):5. PMID: 23351881 – Hassanzadeh, O., Zhu, Qian., Freimuth, RR., Boyce R. Extending the “Web of Drug Identity” with Knowledge Extracted from United States Product Labels. Proceedings of the 2013 AMIA Summit on Translational Bioinformatics. San Francisco, March 2013. – Boyce, RD., Freimuth, RR., Romagnoli, KM., Pummer, T., Hochheiser, H., Empey, PE. Toward semantic modeling of pharmacogenomic knowledge for clinical and translational decision support. Proceedings of the 2013 AMIA Summit on Translational Bioinformatics. San Francisco, March 2013. .
  27. 27. Research Team and others who have contributed University of Pittsburgh Department of Biomedical Informatics: •Harry Hochheiser, Katrina M. Romagnoli, Yifan Ning, Andres Hernandez University of Pittsburgh School of Pharmacy •Philip E. Empey, Solomon Adams W3C Health Care and Life Sciences Interest Group •Michel Dumontier, Jodi Schneider, Maria Liakata, Anita DeWaard, Joanne Luciano, Oktie Hassanzadeh Other researchers •Qian Zhu (U of Maryland), Serkan Ayvaz (Kent State), Majid Rastegar-Mojarad (UW-Milwaukee) 27 Biomedical Informatics
  28. 28. Acknowledgements • Grant funding for the research: – National Library of Medicine (R01LM011838-01), The National Institute of Aging (K01 AG044433-01), NIH/NCATS (KL2TR000146), NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research Network), NIH/NLM (T15 LM007059-24) – Fogarty International Center of Global Health of the National Institutes of Health under the grant No. 1D43TW008443-0 – Agency for Healthcare Research and Quality (K12HS019461). – U of Pitt Institute for Personalized Medicine (PreCISE-Rx: Pharmacogenomics-guided Care to Improve the Safety and Effectiveness of Medications) 28 Biomedical Informatics
  29. 29. Discussion/questions 29 Biomedical Informatics

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