An overview of the research aims for the National Library of Medicine funded research project titled "Addressing gaps in clinically useful evidence on drug-drug interactions" (1R01LM011838-01)

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Addressing Gaps in
Clinically Useful
Evidence on Drug-drug
Interactions – overview
of aims
Richard Boyce, PhD
University of Pittsburgh
Department of Biomedical Informatics
NLM Training Conference
June 18th 2014

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My Lab - Translational Informatics
Applied to Drug Safety (TRIADS)
http://www.dbmi.pitt.edu/content/triads

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The focus of todays “Show Case”
Improving drug-drug interaction knowledge
representation and information retrieval
National Library of Medicine
(1R01LM011838-01)

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What is a drug-drug interaction
• Drug-drug interaction:
– a clinically meaningful alteration of the
effect of a drug (object drug) occurs as a
result of coadministration of another drug
(precipitant drug) [1]
• Potential drug-drug interaction (PDDI):
– two drugs known to interact are prescribed
whether or not harm ensues [1]
1.Hines LE, Malone DC, Murphy JE. Recommendations for Generating, Evaluating, and
Implementing Drug-Drug Interaction Evidence. Pharmacotherapy: The Journal of Human
Pharmacology and Drug Therapy. 2012;32(4):304–313.

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The clinical importance of PDDIs
• Exposure to PDDIs is a significant
source of preventable drug-related harm
[2,3]
• Studies of drug-drug interactions
– Harm 1.9 to 5 million inpatients per year
– Cause 2,600 to 220,000 emergency
department visits per year
2. Magro L, Moretti U, Leone R. Epidemiology and characteristics of adverse drug reactions
caused by drug-drug interactions. Expert Opin Drug Saf. 2012 Jan;11(1):83-94. doi:
10.1517/14740338.2012.631910. Epub 2011 Oct 25. Review. PubMed PMID: 22022824.
3. http://www.cdc.gov/nchs/fastats/ervisits.htm, http://www.cdc.gov/nchs/fastats/hospital.htm
Last Accessed 12/06/2013

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Knowledge is important
Borrowed from Phil Hansten and John Horn

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Key point
• Many drug information systems disagree
about PDDIs
– the specific ones that exist
– their potential to cause harm
• This leads to
– confusion and frustration for clinicians
– greater risks of harm to patients

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The danger of incomplete drug-
drug interaction knowledge

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Evidence of drug compendia problems
• Three PDDI information sources agreed upon only
25% of 59 contraindicated drug pairs found in
black box warnings [28]
• 18 (28%) of 64 pharmacy information and clinical
decisions support systems correctly identified 13
clinically significant DDIs [29]
• Four sources agreed on only 2.2% of 406 PDDIs
considered to be “major” by at least one source
[30]

10. Evidence from the drug compendium
perspective
Pre-market studies Post-market studies
Product labeling
Reported in
Clinical experience
Scientific
literature
Rarely reported in
Rarely reported in
Reported in
Rarely reported in
Drug Compendia synthesize PDDI
evidence into knowledge but
• May fail to include important PDDIs
• Often disagree about PDDI evidence
and seriousness ranking
• May include numerous PDDIs with
little evidence for liability reasons
Source for
Source for

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There is a need for a new PDDI
knowledge representation paradigm
This paradigm should do for PDDIs what the
Pharmacogenomics Knowledge Base
(PharmGKB) and Pharmacogenomics Research
Network (PGRN) have done for clinical
pharmacogenomics

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PharmGKB as inspiration for a new
drug interaction knowledge base (DIKB)
• PharmGKB
– A single point of entry
to nearly all relevant
pharmacogenomics
research
– A network of
researchers and
stakeholders
– A growing set of
clinical
pharmacogenomics
guidelines
• Future DIKB
– A single point of entry
to nearly all relevant
DDI research and
case reports
– A network of
researchers and
stakeholders
– A growing set of
clinical guidelines for
PDDI exposure

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Informatics foundations for a new
DIKB: Aim 1
1. Derive a new PDDI meta-data standard that can meet the
information needs of drug compendia editors and pharmacist
working in different care settings
– the best thinking of drug information system designers and the
biomedical ontology community
– extends existing national drug terminology efforts
– will have a high likelihood of widespread adoption
Pre-market studies Post-market studies Clinical experience
A framework for representing PDDI assertions and evidence as
interoperable Linked Data available for community annotation
Semantic annotation pipeline

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Aim 1 – Highlights of the approach
• …the best thinking of drug information system designers
and the biomedical ontology community
• a new OBO ontology for PDDIs and evidence
• grounded competency questions
– qualitative analysis of interviews with clinical pharmacists, drug
compendia editors, and the results of a systematic search of
the literature
• …extends existing national drug terminology efforts
• interoperability with RxNorm and the NDF-RT
• …will have a high likelihood of widespread adoption
• stakeholders from FDA, NLM, W3C, Pharma, and
Cochrane Collaboration

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http://goo.gl/232LS2

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Informatics foundations for a new
DIKB: Aim 2
2. Apply a novel evidence synthesis process to enhance drug
product label PDDI information
– implement a pipeline for extracting PDDI mentions from product
labeling and integrating them with other public sources
– annotations can be “curated” by a distributed group of drug
experts and non-experts
– dynamically enhance product label content
A framework for representing PDDI
assertions and evidence as
interoperable Linked Data available for
community annotation
Data driven:
• Synthesis of public
PDDI sources
Expert:
• Web-based scientific
discourse
Knowledge
curationAim 1
Aim 2

17. Aim 2 - a step toward the next generation
of drug product labeling
PDDI Extraction
algorithm
Lovastatin
product label
Human curation
Semantic tags
Linking to other
relevant
sources

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Take the drug interactions section of a
drug product label…

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Make it simple for the reader to see
claims that could expand or update the
information in this section…

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Example: an interaction affecting venlafaxine that may
not be in this section…

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Aim 2 – Highlights of the approach

22. Aim 2 – Highlights of the approach
continued
• PDDI information interlinking
• Drug name mapping across sources [1]
• Identification and merging of PDDI public information
sources [2]
• Advancing PDDI evidence reviews
• A “Micropublication” model for drug-drug interaction
evidence [3]
1. Hassanzadeh O, Zhu Q, Freimuth R, Boyce R. Extending the "Web of Drug Identity" with Knowledge
Extracted from United States Product Labels. AMIA Summits Transl Sci Proc. 2013 Mar 18;2013:64-68.
PubMed PMID: 24303301; PMCID: PMC3814463
2. Ayvaz S., Zhu Q., Hochheiser H., Brochhausen M., Horn, J., Dumontier, M., Samwald M., Boyce, RD.
“Drug-Drug Interaction Data Source Survey and Linking.” Abstract and Poster presentation to appear in AMIA
Summits Transl Sci Proc. 2014.
3. Schneider, J., Collins, C., Hines, L., Horn, JR, Boyce, R. “Modeling Arguments in Scientific Papers.” at the
12th Annual ArgDiaP Conference: From Real Data to Argument Mining. Warsaw, Poland, May 23-24 2014.
http://jodischneider.com/pubs/argdiap2014.pdf

23. SPL/DailyMed Jamboree
Workshop
Using DailyMed Drug Product Label Data
September 18, 9:30 AM to 4:15 PM
Lister Hill Auditorium, National
Library of Medicine
Topics include:
• extracting indication and drug interaction data from
structured product labels using natural language processing
• Linked Data and structured product labels
http://goo.gl/3rZH9N

24. Informatics foundations for a new DIKB:
Aim 3
3. Pilot test new methods for PDDI information
retrieval supporting drug information experts
• Develop a high performance PDDI information
retrieval algorithm
• Develop and iteratively refine multiple initial
prototypes based on feedback from end users
• Report on a single end-user validated design
implemented for public demonstration

25. Informatics foundations for a new DIKB
Product labeling
Scientific
literature
A framework for representing PDDI
assertions and evidence as
interoperable Linked Data available for
community annotation
Semantic annotation
pipeline
Reduced risk of
a PDDI
medication
error!
More efficient synthesis of PDDI
evidence, easier identification of gaps
Expected benefits:
• More complete and accurate PDDI
evidence
• Better informed pharmacists and other
clinicians
• More effective PDDI alerting and decisions
support systems
Data driven:
• Synthesis of public
PDDI sources
Expert:
• Web-based scientific
discourse
Knowledge
curation
Dynamic enhancements

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Acknowledgements - People
• Co-investigators: Harry Hochheiser, Phil Empey, Carol Collins
(UW Seattle), John Horn (UW Seattle), Dan Malone (U of A),
Lisa Hines (U of A), William Hogan (UAMS), Mathias
Brochhausen (UAMS)
• Programmer: Yifan Ning
• Students and Research assistants: Katrina Romagnoli, Andres
Hernandez Camacho, Jeremy Jao, Serkan Ayvaz (Kent State),
Majid Rastegar-Mojarad (Mayo)
• Advisors: Rebecca Crowley, Steven Handler, Chip Reynolds,
Jordan Karp, Wendy Chapman (U of Utah), Tim Clark and Paulo
Ciccarese (Harvard), Robert Freimuth (Mayo, PGRN), Qian Zhu
(U of Maryland)
• Additional stakeholders: FDA, Cochrane, W3C Health Care and
Life Sciences Interest Group, ASHP, IBM Research

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Acknowledgements - Funding
• The American taxpayers via:
– NLM (1R01LM011838-01 and T15 LM007059-24)
– NIH/NIA (K01AG044433-01, K07AG033174)
– Agency for Healthcare Research and Quality
(K12HS019461 and R01HS018721)
– NIH/NCATS (KL2TR000146)
– NIH/NIGMS (U19 GM61388; the Pharmacogenomic
Research Network)

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Discussion