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

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. 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. …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. “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
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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. How it works – step 1
• Start with SPL documents
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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>
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SPL setid XXX
sertraline
active moiety

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
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SPL setid XXX
active moiety
sertraline
Ioflupane I-123

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
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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.
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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)
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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. Proof of concept – Clinical Studies mashup

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. Proof of concept – Clinical Studies validation
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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. Proof of concept – Drug Interactions mashup

18. Proof of concept – Drug Interactions preliminary
exploration
• How often the link provide more complete
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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. 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
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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. 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
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• Pharmacist review
– Several true positives
• e.g., escitalopram-tapentadol, escitalopram-metoclopramide
– Some false positives
• e.g., nefazodone-digoxin (digitalis)

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
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case of citalopram)

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
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23. LinkedSPLs – A research program
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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]))
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25. LinkedSPLs – A research program
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Annotations
would go here!

26. Want more information?
• LinkedSPLs on GitHub
– https://github.com/bio2rdf/bio2rdf-scripts/tree/release3/linkedSPLs
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• 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. 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)
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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)
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29. Discussion/questions
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