The document discusses the importance of pharmacogenomic knowledge in clinical settings, focusing on how physicians like Lauren can quickly interpret pharmacogenomic test results to assess medication implications for their patients. It highlights the significance of structured product labeling (SPL) and the need for a semantic model to organize pharmacogenomic information efficiently. The goals also include developing an annotation system in collaboration with pharmacists to enhance the usability of pharmacogenomic data during clinical decision-making.

1. Toward semantic modeling of
pharmacogenomic knowledge
for clinical and translational
decision support
Richard Boyce*, University of Pittsburgh
Robert Freimuth, Mayo Clinic
Katrina M. Romagnoli, University of Pittsburgh
Tara Pummer, University of Pittsburgh
Harry Hochheiser, University of Pittsburgh
Philip E. Empey, University of Pittsburgh
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Department of Biomedical Informatics

2. 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 CYP2D6*2X2.
• Lauren wants to quickly know what the
implications are for each drug that her
patient is taking
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3. What does she need to know?
For each drug ?d taken by her patient,
who carries the CYP2D6*2X2 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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4. Where can she look?
• Local clinical setting
– Institution/clinic specific information
– Likely to be very few in number
• Scientific literature
– CPIC and Dutch pharmacogenomics working
group guidelines
– High quality but may be infrequently updated
– Available in PharmGKB, does she know that?
• Product Labeling
– Concise, clinically oriented information
– Possibly the first place many clinicians look
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5. This talk is going focus on product
labeling…
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6. 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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7. The current status of pharmgx
statements in labeling
• March 2013 [1]:
– 38 biomarkers
– 107 active ingredients
• > 1000 products
– 19 indications
1. FDA. Clinical Pharmacogenomics: Premarketing Evaluation in Early Phase Clinical Studies. Rockville, MD: Federal Drug
Administration; 2011.
2. FDA. Table of Pharmacogenomic Biomarkers in Drug Labels. 2012. Available at:
http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm.
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8. Examples
“For HLA-B*5701-positive
patients, initiating or
reinitiating treatment with an
abacavir-containing regimen
is not recommended”
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9. Examples
“Nursing mothers who are
ultra-rapid metabolizers may
also experience overdose
symptoms such as extreme
sleepiness, confusion, or
shallow breathing.”
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10. Examples
“Patients with low or absent
TPMT activity are at an
increased risk of developing
severe, life-threatening
myelotoxicity if receiving
conventional doses of
azathioprine.”
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11. Examples
“There is no relevant effect of
genetic variation in CYP2B6,
CYP2C9, CYP2C19, or CYP3A5
on the pharmacokinetics of
prasugrel's active metabolite or
its inhibition”
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12. Problem statement
Pharmacogenomics knowledge written in product
labeling is important for clinical use cases and
complements other resources.
However, this knowledge is currently
unstructured which presents barriers to its
efficient acquisition and use in the clinical setting
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13. Our approach
• Work with pharmacists to:
– Develop a semantic model for
pharmacogenomics statements
– Train them on how to annotate the
statements
• Publish annotated statements using
the Open Annotation standard [1]
• Integrate the annotated statements
with other sources of pharmgx
information
– an interactive prototype
1. http://www.openannotation.org/spec/core/
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14. What is “Open Annotation”
• An extensible and interoperable
framework for expressing annotations
[1]
1. http://www.openannotation.org/spec/core/
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15. Open Data Annotation example
“Nursing mothers who are ultra-rapid metabolizers may
also experience overdose symptoms such as extreme
sleepiness, confusion, or shallow breathing.”
ex:annotation-1
ex:body-1 ex:target-1
about
Predicate Object Predicate Object
drug CODEINE hasSource URL to product label
biomarker CYP2D6 Exact-text “Nursing mothers…”
variant Ultra-rapid Preceding- …
metabolizer text
Pharmacokinetic Metabolism-increase Post-text …
effect
Pharmacodynamic Drug-toxicity-risk-
effect increase
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16. Semantic model - descriptions
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17. Description example
“Nursing mothers who are ultra-rapid metabolizers may
also experience overdose symptoms such as extreme
sleepiness, confusion, or shallow breathing.”
ex:annotation-1
ex:body-1 ex:target-1
about
Predicate Object Predicate Object
drug CODEINE hasSource URL to product label
biomarker CYP2D6 Exact-text “Nursing mothers…”
variant Ultra-rapid Preceding- …
metabolizer text
Pharmacokinetic Metabolism- Post-text …
effect increase
Pharmacodynamic Drug-toxicity-risk-
effect increase
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18. Semantic model - recommendations
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19. Recommendation example
““For HLA-B*5701-positive patients, initiating or reinitiating
treatment with an abacavir-containing regimen is not
recommended”
ex:annotation-1
ex:body-1 ex:target-1
about
Predicate Object Predicate Object
drug codeine hasSource URL to product label
biomarker HLA-B*5701 Exact-text “For HLA-B*5701…”
variant HLA-B*5701 Preceding- …
drug-selection- do-not-restart text
recommendation Post-text …
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20. Semantic model - references
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21. Progress
• Since September 2012:
• Further annotation training
• Improved agreement
• Finalizing first-round annotations (~140)
• Proof-of-concept dataset
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22. Want more information?
• Proof-of-concept:
– Dataset (RDF/XML): http://goo.gl/Spd0p
– Example queries: http://goo.gl/3pQ8q (under GRAPH:
http://purl.org/net/nlprepository/spl-pharmgx-annotation-poc)
– Integration with W3C Genomic-CDS: https://genomic-
cds.googlecode.com/svn
• Google code project
– code.google.com/p/swat-4-med-safety/
• Open Data Anotation
– http://www.openannotation.org/spec/core/
• Semantic Science Integrated Ontology
– http://code.google.com/p/semanticscience/
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23. Acknowledgements
• Agency for Healthcare Research and
Quality (K12HS019461).
• NIH/NCATS (KL2TR000146),
• NIH/NIGMS (U19 GM61388; the
Pharmacogenomic Research Network)
• NIH/NLM (T15 LM007059-24)
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24. Backup Slides
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25. 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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26. More about SPLs
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