Biomedical Informatics1

Linked Data and SPLs – Strengths and Limitations

Department of Biomedical Informatics

Richard D. Boyce, PhD University of Pittsburgh

2014 DailyMed Jamboree Public WorkshopSeptember, 18th 2014

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.

…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.

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“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

Claims present in Semantic Web resources

Product labelsScientific literaturePre-market dataPost-market data

Linked SPLs

Customized views

Maintainers of tertiaryDrug information sources

PharmacistsPharmaDrug safety specialistRegulatorsDecisions support tools

Tertiarysource

Architecture

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How it works – step 1• Start with SPL documents

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How it works – step 2• Convert each part of an SPL document to a

“triple”

predicate

• The subject, predicate, and objects are specified by special identifiers called “URIs”

<http://.../setid-XXX> <http://.../activeMoiety> <http://../sertraline>

subject object

SPL setid XXX

sertraline

active moiety

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• 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>

How it works – step 3

SPL setid XXX

sertraline

active moiety

Ioflupane I-123

Potentially interacts with

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• Make the new “Linked SPLs” data source accessible on the Internet to enable cross-resource queries

How it works – step 4

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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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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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)

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

2. having at least one published result indexed in PubMed

• based on a linkedct:trial_results_reference property pointing to a PubMed identifier

1. http://linkedct.org/ Last Accessed 09/17/20142. 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

SPL setid XXX sertralineactive moiety

DB01104DrugBank ID

Linked CT YYY DB01104see also

PMID ZZZ

trial resultNLP used to extract conclusions [3]

Proof of concept – Clinical Studies mashup

LinkedCT02/2000 –

2/2012

Retrieved 170 records from

PubMed

SPARQL: Get PubMed ID for all “results references”

for studies involving a random sample of 9 psychotropic drugs

eUtils: Get records for all PubMed IDs

Conclusionsmanually 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

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Proof of concept – Clinical Studies validation

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]

2. Indicated as having an “Active” status in the NDF-RT

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/

SPL setid XXX sertralineactive moiety

NDF-RT YYY interactionkind of record

NDF-RT ZZZhas participant

sertraline

preferred label (i.e., name)

activerecord status

Proof of concept – Drug Interactions mashup

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Proof of concept – Drug Interactions preliminary exploration

• How often the link provide more complete 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

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Product label DDIs for 20 drugs manually identified [22]• ~70 interactions • Pharmacokinetic and pharmacodynamic

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

Filter out DDIs previously

identified in antidepressant product labels

Proof of concept – Drug Interactions preliminary exploration cont…

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• 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

• Pharmacist review– Several true positives

• e.g., escitalopram-tapentadol, escitalopram-metoclopramide

– Some false positives

• e.g., nefazodone-digoxin (digitalis)

Proof of concept – Drug Interactions preliminary exploration cont…

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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 case of citalopram)

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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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LinkedSPLs – A research program

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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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LinkedSPLs – A research program

Annotations would go here!

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Want more information?• LinkedSPLs on GitHub

– https://github.com/bio2rdf/bio2rdf-scripts/tree/release3/linkedSPLs

• 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. .

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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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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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Discussion/questions