Biomedical Informatics1

Extending the “Web of Drug Identity” with Knowledge Extracted from United States Product LabelsOktie Hassanzadeh, IBM ResearchQian Zhu, Mayo ClinicRobert Freimuth, Mayo ClinicRichard Boyce*, University of Pittsburgh

Department of Biomedical Informatics

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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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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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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

2. Until recently, there was no requirement to publish pre-market drug study results

– This has changed since ~2010

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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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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, cimetidine-citalopram, and venlafaxine-haloperidol. [2-5]

1. Physicians’ Desk Reference, 66th Edition. 2012 Edition. PDR Network; 2011.2. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=b1de3ed9-1cb8-e419-3f25-5b0aeed5779a. Accessed November 27, 2012.3. http://dailymed.nlm.nih.gov/dailymed/lookup.cfm?setid=cf2d9bee-f8e3-477a-e4b4-f0e82657b7d2. Accessed November 27, 2012.4. 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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Second take home point:

• All American product labeling content is available in an accessible format– Structured Product Labeling (SPL)

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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.pdf2. http://www.fda.gov/ForIndustry/DataStandards/StructuredProductLabeling/default.htm3. http://dailymed.nlm.nih.gov/dailymed/downloadLabels.cfm

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More about SPLs

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More about SPLs

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Third take home point

• LinkedSPLs is a Linked Data version of SPLs– simplifies access to SPL content

– interoperable with other important drug terminologies

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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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LinkedSPLs – A research program

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LinkedSPLs – A research program

Your annotations would go here!

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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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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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Example

prodName rxNormProduct epcClass contraindications

Nefazodone Hydrochloride

rxcui:1098666 SEROTONIN REUPTAKE INHIBITOR

CONTRAINDICATIONS Coadministration of terfenadine, astemizole, cisapride, pimozide, or carbamazepine with nefazodone hydrochloride is contraindicated….

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What we tested• Three different linking approaches to link

to DrugBank1. 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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Linkage to DrugBank – Results

InChI identifier

ChEBI identifier

InChI + ChEBI

Automatic

InChI identifier 424 261 424 395

ChEBI identifier --- 707 707 650

InChI + ChEBI -- -- 831 791

Automatic -- -- -- 1162

• 1,246 active ingredients could be mapped to DrugBank by at least one method

• 1,096 unmapped ingredients

• The three approaches complement each other

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• 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.

Conclusions

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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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Acknowledgements

• NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research Network)

• Agency for Healthcare Research and Quality (K12HS019461).

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Backup Slides

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Linkage in LinkedSPLs

An active ingredient from an SPL

Active ingredient resource in Linked SPLs

SPL resource

dailymed:activeMoiety“OLANZAPINE”

“N7U69T4SZR”dailymed:activeMoietyUNII

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Linkage to DrugBank – Approach 1

1. FDA UNII table provides structure string:

2. NCI Resolver provides InChIKey:

3. DrugBank record with the above InChIKey provides identifier:

Results:429 out of 2,264 ingredients are linked, out of which 424 are

valid

“N7U69T4SZR”

Starting with UNII….

2-METHYL-4-(4-METHYL-1-PIPERAZINYL)-10H-THIENO(2,3-B)(1,5)BENZODIAZEPINE

KVWDHTXUZHCGIO-UHFFFAOYSA-N

DB00334

Idea: Using NCI Resolver & InChIKey

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Linkage to DrugBank – Approach 2

“OLANZAPINE”

1. ChEBI preferred name from NCBO Bioportal:

2. ChEBI identifier from NCBO Bioportal:

3. DrugBank record with the above ChEBI identifier provides identifier:

Results:718 out of 2,264 ingredients are linked, out of which 707 are

valid

“OLANZAPINE”

7735

DB00334

Idea: Using ChEBI identifier & NCBO Portal

Starting with name….

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Linkage to DrugBank – Approach 3

Starting with all data in the FDA UNII table and DrugBank….

1. Index all FDA UNII table and DrugBank XML attributes2. 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 score4. 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

“N7U69T4SZR”

UNII

“OLANZAPINE”

Preferred Substance Name

“2-METHYL-4….”

Molecular Formula

“ZYPREXA”

synonym

Idea: Automatic discovery of linkage points

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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