Report on Linked Structured Product Labels (LinkedSPLs) and a study evaluating three different approaches to mapping active ingredients coded in Structured Product Labels to DrugBank.

1. Extending the “Web of Drug
Identity” with Knowledge
Extracted from United States
Product Labels
Oktie Hassanzadeh, IBM Research
Qian Zhu, Mayo Clinic
Robert Freimuth, Mayo Clinic
Richard Boyce*, University of Pittsburgh
1 Biomedical Informatics
Department of Biomedical Informatics

2. Take home message
• Drug product labeling is a vital, unique, and
under-utilized source of claims and evidence
about drugs
– genes, diseases, drugs, drug interactions, special
populations, and adverse reactions
• All American product labeling content is
available in an accessible format
– Structured Product Labeling (SPL)
• LinkedSPLs is a Linked Data version of SPLs
– simplifies access to SPL content
– interoperable with other important drug
terminologies
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3. Drug product labeling is special?
• It complements existing knowledge sources
– 40% of 44 pharmacokinetic drug-drug
interactions affecting 25 drugs were located
exclusively in product labeling [1]
– 24% of clinical efficacy trials for 90 drugs were
discussed in the product label but not the
scientific literature [2]
– 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.
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4. Why product labeling has information
that is not in the scientific literature
1. Product labels contain a summary of
information reported in detail in a
drug’s New Drug Application
– Often difficult/impossible for a
researcher to access
1. Until recently, there was no
requirement to publish pre-market
drug study results
– This has changed since ~2010
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5. Product labeling is under-utilized
by translational researchers
• only two out of more than 2,300
MEDLINE abstracts discuss product
label NLP [1]
• Several recent informatics projects
did not explicitly include product label
information [2-6]
1. Query done on 11/26: (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]))
2. Segura-Bedmar I, Martinez P, Sanchez-Cisneros D eds. Proceedings of the First Challenge Task: Drug-Drug Interaction Extraction 2011. Huelva, Spain; 2011.
Available at: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-761/. Accessed December 9, 2011.
3. 16. SEMEVAL. Task Description - Extraction of Drug-Drug Interactions from BioMedical Texts. 2012. Available at: http://www.cs.york.ac.uk/semeval-2013/task9/.
Accessed November 20, 2012.
4. Percha B, Garten Y, Altman RB. Discovery and explanation of drug-drug interactions via text mining. Pac Symp Biocomput. 2012:410–421.
5. Tari L, Anwar S, Liang S, Cai J, Baral C. Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.
Bioinformatics. 2010;26(18):i547–553.
6. Duke JD, Han X, Wang Z, et al. Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated
drug interactions. PLoS computational biology. 2012;8(8):e1002614.
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6. Doesn’t DrugBank handle this?
• Not really!
– DrugBank includes product label content from
the Physicians’ Desk Reference (PDR) [1]
– However, the PDR is actually a subset of
available product label content
• claims and evidence unique to those drug product
labels not included in the PDR will be missing from
DrugBank
• potential negative effects on informatics experiments
that that require complete drug information.
• E.g., possibly missed drug-interactions (DrugBank 3.0)
include cimetidine-sertraline, cimetidine-venlafaxine,
http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=b1de3ed9-1cb8-e419-3f25-5b0aeed5779a. Accessed November 27, 2012. [2-
cimetidine-citalopram, and venlafaxine-haloperidol.
1. Physicians’ Desk Reference, 66th Edition. 2012 Edition. PDR Network; 2011.
2.
3. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=cf2d9bee-f8e3-477a-e4b4-f0e82657b7d2. Accessed November 27, 2012.
4. 5]
http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=4259d9b1-de34-43a4-85a8-41dd214e9177. Accessed November 27, 2012.
5. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=53c3e7ac-1852-4d70-d2b6-4fca819acf26. Accessed November 27, 2012.
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7. Second take home point:
• All American product labeling content
is available in an accessible format
– Structured Product Labeling (SPL)
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8. Structured Product Labels (SPLs)
• What you would see if you downloaded an
SPL from DailyMed
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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9. More about SPLs
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10. More about SPLs
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11. Third take home point
• LinkedSPLs is a Linked Data version of
SPLs
– simplifies access to SPL content
– interoperable with other important drug
terminologies
11 Biomedical Informatics

12. LinkedSPLs – hypothesis
Hypothesis: A Linked Data knowledge base of
drug product labels with accurate links to other
relevant sources of drug information will provide a
dynamic platform for drug information NLP that
provides real value to translational researchers
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13. LinkedSPLs – A research program
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14. LinkedSPLs – A research program
Your annotations
would go here!
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15. LinkedSPLs – Method
• Currently we are focusing on
linking active ingredients in the
structured portion of SPLs
• unstructured text for future
work
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16. Linkage to external sources
• There are many sources of drug information
that are complementary to each other.
– DrugBank: contains drug targets, pathways,
interactions
– RxNorm: provides UMLS mappings
– ChEBI: provides rigorous classification of drugs
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17. Example
prodName rxNormProduct epcClass contraindications
Nefazodone rxcui:1098666 SEROTONIN CONTRAINDICATIONS
Hydrochloride REUPTAKE Coadministration of
INHIBITOR terfenadine, astemizole,
cisapride, pimozide, or
carbamazepine with
nefazodone hydrochloride
is contraindicated….
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18. What we tested
• Three different linking approaches to link
to DrugBank
1. Structure string (InChI)
2. Ontology label matching (ChEBI)
3. Unsupervised linkage point discovery
(Automated) [1]
1. O. Hassanzadeh et al. “Discovering Linkage Points over Web Data”. To Appear in PVLDB, Vol
6. Issue 6, August 2013
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19. Linkage to DrugBank – Results
• 1,246 active ingredients could be mapped to
DrugBank by at least one method
• 1,096 unmapped ingredients
• The three approaches complement each other
InChI ChEBI InChI + Automatic
identifier identifier ChEBI
InChI identifier 424 261 424 395
ChEBI identifier --- 707 707 650
InChI + ChEBI -- -- 831 791
Automatic -- -- -- 1162
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20. Conclusions
• The automatic approach performs very well
– A greater number of accurate links discovered
with less effort
• A significant number remain unmapped:
– Some salt or racemic forms of mapped ingredients
(e.g., alpha tocopherol acetate D)
– Elements (e.g., gold, iodine), and variety of natural
organic compounds including pollens (N~200)
• Not all ingredients are included in DrugBank
– other resources may be required to obtain
complete mappings for active ingredients.
20 Biomedical Informatics

21. Want more information?
• LinkedSPLs
– http://purl.org/LinkedSPLs
• Google code project
– code.google.com/p/swat-4-med-safety/
• Publications
– 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.
– 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.
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22. Acknowledgements
• NIH/NIGMS (U19 GM61388; the
Pharmacogenomic Research Network)
• Agency for Healthcare Research and
Quality (K12HS019461).
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23. Backup Slides
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24. Linkage in LinkedSPLs
An active ingredient from an SPL
Active ingredient resource in Linked SPLs
dailymed:activeMoiety
SPL resource “OLANZAPINE”
dailymed:activeMoietyUNII
“N7U69T4SZR”
24 Biomedical Informatics

25. Linkage to DrugBank – Approach 1
Starting with UNII….
“N7U69T4SZR” Idea: Using NCI Resolver & InChIKey
1. FDA UNII table provides structure string:
2-METHYL-4-(4-METHYL-1-PIPERAZINYL)-10H-THIENO(2,3-B)(1,5)BENZODIAZEPINE
2. NCI Resolver provides InChIKey:
KVWDHTXUZHCGIO-UHFFFAOYSA-N
3. DrugBank record with the above InChIKey provides
identifier: DB00334
Results:
429 out of 2,264 ingredients are linked, out of which 424 are
valid
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26. Linkage to DrugBank – Approach 2
Starting with name….
“OLANZAPINE” Idea: Using ChEBI identifier & NCBO Portal
1. ChEBI preferred name from NCBO Bioportal:
“OLANZAPINE”
2. ChEBI identifier from NCBO Bioportal:
7735
3. DrugBank record with the above ChEBI identifier provides
identifier: DB00334
Results:
718 out of 2,264 ingredients are linked, out of which 707 are
valid
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27. Linkage to DrugBank – Approach 3
Starting with all data in the FDA UNII table and DrugBank….
Preferred Substance Name
Molecular Formula
“OLANZAPINE” Idea:
“2-METHYL-4….”
Automatic discovery of
UNII
“N7U69T4SZR”
synonym
linkage points
“ZYPREXA”
1. Index all FDA UNII table and DrugBank XML attributes
2. Search for linkage points and score similarity:
UNII -> Substance Name  DrugBank -> brands -> brand: 0.94
UNII -> Preferred Substance Name  DrugBank -> name : 0.91
UNII -> Substance Name  DrugBank -> synonyms -> synonym : 0.83
…
3. Prune list of linkage points based on cardinality, coverage, and average score
4. Establish links between FDA UNII table and DrugBank using the linkage points
UNII “OLANZAPINE”   DrugBank “Zyprexa” : 1.0
…
Results: 1,179 out of 2,264 ingredients are linked, out of which 1,169 are valid
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28. Linkage Point Discovery Framework
• A generic framework for unsupervised discovery
of linkage points
Details can be found at:
O. Hassanzadeh et al. “Discovering Linkage Points over Web Data”. To Appear in
PVLDB, Vol 6. Issue 6, August 2013
28 Biomedical Informatics