The document discusses expanding the structured product label (SPL) model to include annotations of pharmacogenomic information. Currently, SPLs contain unstructured text descriptions of pharmacogenomic biomarkers and their implications for drugs. Annotations could normalize this information by linking specific drug-biomarker-recommendation relationships. This would enable more advanced decision support and knowledge discovery. The presentation provides an example of how pharmacists are annotating labels to express pharmacogenomic statements as structured data using the Open Annotation model.

1. Expanding the SPL Model Beyond
Indexing to Include Annotations
Richard D. Boyce, PhD
University of Pittsburgh
2014 DailyMed Jamboree Public Workshop
September, 18th 2014
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Department of Biomedical Informatics

2. What is annotation?
• “Annotation” involves turning unstructured
text descriptions into a normalized data
structure
– Different from “indexing” which simply tags
mentioned entities
“Drug X indicated for condition Y”
– Indexing: tags Drug X and Condition Y to aid
information retrieval
• does not retain the relationship “indicated for”
– Annotation: retains the relationship
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3. “Take home” point
• Annotation of specific content written into
SPLs will enable innovative downstream
uses
– Decision support
– Drug safety
– Drug discovery / repositioning
• Pharmacogenomics will be used as a case
study
– Other potentially relevant kinds of information
exist
• drug-drug interactions, adverse drug
reactions
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4. Structured Product Labels (SPLs)
• 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
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5. NLM DailyMed
SPL database
Pharma
contributes
SPLs to FDA
Active and inactive
ingredients
Represented
organization
Route of
administration
Names (trade name
and generic)
SPL identification
(setid, version,
effective date)
LOINC-coded
sections
Boxed
Warnings
Clinical Studies
Clinical pharmacology
Adverse
Reactions
Drug interactions
…other
sections…
FDA indexing
(pharmacologic
class and billing
unit)
Unstructured text
and HTML tables

6. Scenario
• Lauren is a physician in an outpatient
clinic. She receives a pharmacogenomics
test result for one of her female patients.
• The result states that the patient has the
genotype HLA-B*5701
• Lauren wants to quickly know what the
implications are for each drug that her
patient is taking
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7. What does she need to know?
For each drug ?d taken by her patient,
who carries the HLA-B*5701 genotype,
what is the…
…potential impact
– pharmacokinetic / pharmacodynamic
...patient specific risk factors
– Concomitant medications
– Medical conditions
…recommendations
– dosage, drug administration, alternatives,
monitoring, and tests
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8. FDA’s goals for pharmacogenomics
and product labeling [1]
“Inform prescribers about the impact,
or lack of impact, of genotype on
phenotype”
“Indicate whether a genomic test is
available and if so, whether testing
should be considered, recommended,
or necessary.”
1. FDA. Clinical Pharmacogenomics: Premarketing Evaluation in Early Phase Clinical Studies.
Rockville, MD: Federal Drug Administration; 2011.
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9. Example label pharmgx statements
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10. The current status of pharmgx
statements in labeling
• August 18, 2014 [1]:
– 138 drugs, 43
biomarkers
– 11 drug/biomarker
pairs with boxed
warning or warning
and precaution
mentions
– 19 drug therapeutic
areas 1. FDA. Table of Pharmacogenomic Biomarkers in Drug Labels. 2014. Available at:
http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm. Last accessed 09/15/2014
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11. Pharmgx information within SPLs
Unstructured text statements in
various sections
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12. Finding label pharmgx information (current)
Four clicks from here to a PDF
version of the label
http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.ht
m
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13. Annotation vs indexing
• The FDA biomarker table basically
indexes pharmacogenomics information
– This information could be provided as
supplementary SPL files
– Some basic kinds of queries would be
supported
• But, many clinician use cases would not be met
• What about extending the SPL to
include annotations?
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14. Expanding SPLs to include annotations
The Open Annotation (OA) Data model: An
extensible and interoperable framework for
annotations [1]
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1. http://www.openannotation.org/spec/core/

15. Example pharmgx recommendation
ex:body-1 ex:target-1
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Predicate Object
drug abacavir
biomarker HLA-B*5701
drug-selection-recommendation
do-not-restart
Predicate Object
hasSource URL to product label
Exact-text “For HLA-B*5701…”
Preceding-text
…
Post-text …
ex:annotation-1
about

16. One annotation for many labels
Once created, annotations can be rendered in an OA
client for any label where the statement is written the
same way.
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17. More on the general annotation model
ex:body-1 ex:target-1
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Predicate Object
drug abacavir
biomarker HLA-B*5701
drug-selection-recommendation
do-not-restart
Predicate Object
hasSource URL to product label
Exact-text “For HLA-B*5701…”
Preceding-text
…
Post-text …
ex:annotation-1
about
The use of standard terminologies
enables interoperability
Text selection is not dependent
on a specific document

18. Our multidisciplinary approach
• Work with pharmacists to:
– Understand their information needs
– Develop a semantic model for
pharmacogenomics statements
– Train them on how to annotate the
statements
• using a custom plugin for a web annotation tool
(Domeo [1])
• Develop a prototype decision support
tool [2]
1. Ciccarese P, Ocana M, Clark T. Open semantic annotation of scientific publications using DOMEO. J Biomed Semantics.
2012 Apr 24;3 Suppl 1:S1. doi: 10.1186/2041-1480-3-S1-S1.
2. Boyce, RD., Freimuth, RR., Romagnoli, KM., Pummer, T., Hochheiser, H., Empey, PE. Toward semantic modeling of
pharmacogenomic knowledge for clinical and translational decision support. AMIA Summits Transl Sci Proc. Mar 18 2013:28-
32. PMCID: PMC3814496
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19. Progress
• Semi-structured interviews
– 14 completed with different types of
pharmacists
– Their information needs are informing a
prototype
• Pharmacist annotations on labels for 16
drugs
– To date 8 pharmacist have participated
– Consensus annotations completed for 10 drugs
• Annotations published as an annotation
web service
– in OA at the “LinkedSPLs” RDF endpoint
– more to say about this later today
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20. -----------

22. Revisiting how to find label pharmgx
information
Video of the concept: https://www.youtube.com/watch?v=Te546vOiruo

23. Video of the concept: https://www.youtube.com/watch?v=Te546vOiruo

24. Video of the concept: https://www.youtube.com/watch?v=Te546vOiruo

25. Some lessons learned while annotating
• Which labels to annotate?
– Innovator labels chosen. Further work needed
to determine consistency of content across
labels
• Tables should also be annotated
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– e.g., warfarin dosing
• Some annotations are actually on
dynamically generated sections
– the "Highlights" section is generated by an SPL
stylesheet
– We decided to annotate within the text and
code special (42229-5 SPL UNCLASSIFIED
SECTION)

26. Future work
• Determining the stability of
annotations
– across labels
– over time
• Task based usability studies
– How well are information needs being
met
– What problems do clinicians have with
understanding the information
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27. Want more information?
• Proof-of-concept:
– https://www.youtube.com/watch?v=Te546vOiruo
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• Code project
– LinkedSPLs : https://code.google.com/p/swat-4-med-safety/
– Domeo Pharmgx and drug-drug interaction plugins:
https://github.com/rkboyce/DomeoClient
• Open Data Anotation
– http://www.openannotation.org/spec/core/

28. Research Team
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
Harvard/Mass General (Domeo and the OA
standard)
•Paolo Ciccarese, Tim Clark
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29. Acknowledgements
• Annotators (U of Pitt School of Pharmacy):
– Solomon Adams, Allison Doherty, Jocelyn Hatfield, Alex R.
Cockerham, Linda Huang, Michael Diduch, William Wilson,
Fengyee Zhou
• 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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30. Discussion/questions
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