Addressing gaps in clinically useful evidence on potential drug-drug interactions

Biomedical Informatics1
Addressing gaps in
clinically useful evidence
on drug-drug
interactions
May 2nd
2013
BioDLP Seminar at the
University of Wisconsin - Milwaukee
Richard Boyce, University of Pittsburgh
Department of Biomedical Informatics

Goals for this talk
• Describe potential drug-drug interactions
(PDDIs)
– the significant challenges facing clinicians and
maintainers of drug information systems.
• Present a new PDDI knowledge
representation paradigm
– that I hypothesize will yield more clinically
relevant evidence than is currently possible
• Discuss my BioDLP research
– Within the context of the new paradigm

Part I – PDDIs and challenges for
PDDI knowledge representation

What is a PDDI?
• 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) [10]
• Potential drug-drug interaction
(PDDI):
– two drugs known to interact are
prescribed whether or not harm ensues
[10]
• Pharmacokinetic or

The clinical importance of PDDIs
• PDDIs are a significant source of
preventable drug-related harm
– 13.3% of preventable errors leading to
an ADE [1]
– 7% (23/338) of the ADEs attributable to
PDDIs [2]
– 16 cohort and case-control studies
reported an elevated risk of
hospitalization in elderly patients who
were exposed to PDDIs [3]

Knowledge is important
• Failure to properly manage a PDDI is a
medical error
• The IOM has noted that a lack of drug
knowledge is one of the most frequent
proximal causes such errors [4]

The danger of incomplete drug-
drug interaction knowledge

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

Evidence of drug compendia problems
• Three PDDI information sources agreed upon
only 25% of 59 contraindicated drug pairs
found in black box warnings [5]
• 18 (28%) of 64 pharmacy information and
clinical decisions support systems correctly
identified 13 clinically significant DDIs [6]
• Four sources agreed on only 2.2% of 406
PDDIs considered to be “major” by at least one
source [7]

Why do compendia disagree?
• Four types of information to decide if a PDDI
warrants clinical action. [21]
• Collecting evidence related to each information
item on 244 PDDIs enabled them to determine
that 12% would require no action by physicians
[8]

A conceptual model – 30,000 feet
view
Limits the effectiveness of
PDDI alerting and
CPOE systems
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
PDDI adverse event
Increases the risk of
PDDI evidence
Scattered across numerous sources

PDDI evidence – pre-market studies
Pre-market studies establish PDDI
feasibility but:
•usually do not indicate ADE
seriousness, incidence, or risk
•Focus on generally younger and
healthier populations
•Do not exist for many older drugs
Product labeling
Reported in
Scientific literature
Rarely reported in
See references 31 and 32

PDDI evidence – post-market studies
Post-market studies can provide
evidence of PDDI risk and incidence
if well-designed but:
•rarely are randomized studies due to
ethical considerations
•older drugs less likely to be studied
Product labeling
Reported inRarely reported in

PDDI evidence – Clinical experience
Product labeling
Clinical experience can provide first
warning of a PDDI's and offers
unique insight on PDDI severity:
•are often case reports of low
evidential quality
•there is no general way to collect and
share these insights
Rarely reported in
Rarely reported in

Evidence from the drug compendium
perspective
Pre-market studies Post-market studies
Product labeling
Reported in
Clinical experience
Rarely reported in
Rarely reported in
Reported in
Rarely reported in
Source for
Source for

Effects on the clinician and patient
PDDI alerting and
CPOE systems
PDDI adverse event
Increases the risk of
Limits the effectiveness of

PDDI over-alerting
• Systems that provide PDDI alerts at
the point of care often alert to PDDIs
that have little potential clinical
significance
– frustrating clinicians
“Drug safety alerts are overridden by clinicians
in 49% to 96% of cases” [11]
– can lead to inappropriate responses
“An increased number of non-critical alerts…
was the only variable associated with an
inappropriate provider response” [12]

Summary of challenges for PDDI
knowledge representation
• PDDI evidence is distributed, dynamic, and
of varying quality
• There are significant gaps in PDDI evidence
making it hard to assess
– what is the potential harmful effect?
– who is the PDDI most likely to affect?
– when is a patient most at risk?
• Alerting has to become more
intelligent!

Part II – a new PDDI knowledge
representation paradigm

The new paradigm
Product labeling
A framework for representing PDDI
assertions as interoperable Linked
Data
Pharmacoepidemiology
studies
Semantic
annotation
High priority
PDDIs for
research
Semantic annotation
Reduced risk
of a PDDI
medication
error!
Clinical experience
Better 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

Elements of the new paradigm
• Linked Data [13]
– a Semantic Web technology that makes
distributed knowledge sources
interoperable, with interconnections
providing rich context that would be
unavailable from any single database
• Semantic annotation [14]
– a technology that enhances digital
information artifacts by linking them to
provenance and expert commentary
• Pharmacoepidemiology [15]
– an approach to studying of the use and
effects of drugs in large numbers of people

Linked Data
• What is it?
– 3 minute jargon free introduction:
• player.vimeo.com/video/36752317
• My research has shown Linked Data to
be a potentially effective means of
linking clinical drug information [9]
– Several high quality resources
– More complete information

predicate
Resource Description Framework (RDF)
• Data model – triples
• Syntax – RDF
– The subject, predicate, and objects are
specified by URIs
<http://.../AnneHathaway> <http://.../Married> <http://../Shakespeare>
<http://.../Shakespeare> <http://.../Wrote> <http://../Hamlet>
subject object
AnnHathaway
Shakespeare
Hamlet
married
wrote

Semantic Annotation
http://www.openannotation.org/spec/core/

Combining Linked Data and Semantic Annotation

A structured assessment scores evidence and
potential severity [21]
Pharmacoepidemiology – filling in the gaps

Recap of the new paradigm
Product labeling
Data
Pharmacoepidemiology
studies
Semantic
annotation
High priority
PDDIs for
research
Semantic annotation
Reduced risk
of a PDDI
medication
error!
Clinical experience
Better 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

Anticipated benefits of the new paradigm
• A computable representation of PDDI safety
concerns that is linked to:
– evidence
– expert input, and
– pharmacoepidemiologic study results
• More complete, timely, and accurate PDDI
evidence
– easier integration for drug compendia and CPOE
developers
• Better informed clinicians and patients

Part II – A brief review of my
research within the context of this
paradigm

Overview of my recent PDDI studies
Product labeling
Data
Pharmacoepidemiology studies
Semantic
annotation
High priority PDDIs
for research
Semantic annotation
Clinical experience
Better synthesis of PDDI evidence, easier
identification of gaps
A, B
C
E G
A. Boyce et al. Am J Geriatr Pharmacother. 2012. Apr;10(2):139-50. [22]
B. Boyce et al. Annals of Pharmacotherapy. 2012. Oct;46(10):1287-98 [23]
C. Boyce et al. Proceedings of the 2012. Workshop on BioNLP. 206-213 [16]
D. & E. Boyce et al. Proceedings of the 2013. AMIA Summit on
Translational Bioinformatics. 28-32 (D), 64-68 (E). [18,19]
F. Boyce et al. J Biomed Semantics. 2013. Jan 26;4(1):5. [9]
G. Boyce et al. Poster at Aging Institute Research Day. 2013. [20]
F
D

Linked Data – linking product labels to the
“Web of Drug Identity”
Product labeling
Data
E. Boyce et al. Proceedings of the 2013. AMIA Summit on Translational Bioinformatics. 64-68 [19]
F. Boyce et al. J Biomed Semantics. 2013. Jan 26;4(1):5. [9]
E
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 clinical and translational researchers.
Better synthesis of PDDI evidence, easier
identification of gaps
F
C

Structured Product Labels (SPLs)
• All package inserts for currently marketed
drugs are available as SPLs [27-29]

More about SPLs

Key point
• LinkedSPLs [26] is a Linked Data version
of SPLs
– >36,000 FDA-approved prescription and
over-the-counter drugs present in
DailyMed
– simplifies access to SPL content
– interoperable with other important drug
terminologies and resources
– Enables queries across drug information
resources…

Example cross-resource queries
• 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

LinkedSPLs – A research program

LinkedSPLs – A research program
Your annotations
would go here!

An Example - extracting PDDIs from product
labels Product labeling
C
Recently published NLP algorithm specifically designed to extract PDDIs
from drug product labels
• A drug package insert PK PDDI corpus [24]:
• 592 PK PDDIs,
• 3,351 active ingredients,
• 234 drug product mentions,
• 201 metabolite mentions.
• SVM performed best
• F = 0.859 for pharmacokinetic PDDI identification
• F = 0.949 for modality assignment
• Syntactic information helped with sentences containing both
interacting and non-interacting pairs

Application of the NLP algorithm
• Extract PDDIs and integrate into linked SPLs
PDDI Extraction
algorithm
Lovastatin
product label

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
– VA NDF-RT: PDDIs and drug classification
– ChEBI: provides rigorous classification of drugs

Two linking studies
• Active ingredients in the structured portion of SPLs
to DrugBank [19]
• Three different approaches
• One fully unsupervised
• PDDIs (VA NDF-RT) to the Drug Interactions section
of 26 psychotropics [9]
• What benefits for this linkage?
• Collaboration with Majid Rastegar-Mojarad

Linking Active ingredients in SPLs
to DrugBank
• Three different linking approaches to link
to DrugBank
1. Structure string (InChI)
2. Ontology label matching (ChEBI)
3. Unsupervised linkage point discovery
(Automated) [30]

Linkage to DrugBank – Results
• 1,246 active ingredients (53%) could be mapped to
DrugBank by at least one method
• 1,096 unmapped ingredients
• The three approaches complement each other
InChI
identifier
ChEBI
identifier
InChI +
ChEBI
Automatic
InChI identifier 424 261 424 395
ChEBI identifier --- 707 707 650
InChI + ChEBI -- -- 831 791
Automatic -- -- -- 1162

• 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.
Linking methods conclusions

Linking from VA NDF-RT to LinkedSPLs
• How often would it 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

PDDI studies comparing information sources
Product label PDDIs for 20 drugs manually identified [22]
• ~70 interactions
• Pharmacokinetic and pharmacodynamic
We filtered NDF-RT PDDIs
• String matching and an expanded version of the PDDI 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

Linking from VA NDF-RT - results
• 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 PDDIs 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 mashup

The complete proof of concept
• http://tinyurl.com/c53skm4
– 29 psychotropic drug products
– Created using four Semantic Web nodes
1. http://thedatahub.org/dataset/linked-structured-product-labels
2. http://thedatahub.org/dataset/linkedct
3. http://bioportal.bioontology.org/ontologies/47101
4. http://thedatahub.org/dataset/the-drug-interaction-knowledge-base

Concluding points
• The paradigm provides a framework
for tying together
– NLP for extracting PDDIs
– NLP for linking evidence it to PDDIs
– Aggregating existing PDDI resources in a
single framework
• Research prioritization
• Community engagement

Acknowledgements
• Agency for Healthcare Research and Quality (K12HS019461).
• NIH/NCATS (KL2TR000146),
• NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research
Network)
• NIH/NLM (T15 LM007059-24)
• The Drug Interaction Knowledge Base team
– John Horn Pharm.D, Carol Collins MD, Greg Gardner, Rob
Guzman
• W3C LODD Task Force and the Clinical Genomics Working
Group

References
1. Gurwitz JH, Field TS, Harrold LR, et al. Incidence and preventability
of adverse drug events among older persons in the ambulatory
setting. JAMA. 2003;289(9):1107–1116
2. Gurwitz JH, Field TS, Judge J, et al. The incidence of adverse drug
events in two large academic long-term care facilities. Am. J. Med.
2005;118(3):251–258
3. Hines LE, Murphy JE. Potentially harmful drug-drug interactions in
the elderly: a review. Am J Geriatr Pharmacother. 2011;9(6):364–
377.
4. Committee on Identifying and Preventing Medication Errors, Philip
Aspden, Julie Wolcott, J. Lyle Bootman, Linda R. Cronenwett,
Editors. Preventing Medication Errors: Quality Chasm Series.
Washington, D.C.: The National Academies Press; 2007.
5. Wang LM, Wong M, Lightwood JM, Cheng CM. Black box warning
contraindicated comedications: concordance among three major
drug interaction screening programs. Ann Pharmacother.
2010;44(1):28–34
6. Saverno KR, Hines LE, Warholak TL, et al. Ability of pharmacy
clinical decision-support software to alert users about clinically
important drug-drug interactions. J Am Med Inform Assoc.

References cont.
7. Abarca J, Malone DC, Armstrong EP, et al. Concordance of severity
ratings provided in four drug interaction compendia. J Am Pharm
Assoc (2003). 2004;44(2):136–141.
8. Van Roon EN, Flikweert S, Le Comte M, et al. Clinical relevance of
drug-drug interactions : a structured assessment procedure.  Drug Saf.
2005;28(12):1131–1139.
9. Boyce R, Horn J, Hassanzadeh O, et al. Dynamic Enhancement of
Drug Product Labels to Support Drug Safety, Efficacy, and
Effectiveness. Journal of Biomedical Semantics. Journal of Biomedical
Semantics. 2013. Jan 26;4(1):5.
10. 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.
11. Van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety
alerts in computerized physician order entry. J Am Med Inform Assoc.
2006;13(2):138–147.

References cont.
12. Miller AM, Boro MS, Korman NE, Davoren JB. Provider and
pharmacist responses to warfarin drug-drug interaction alerts: a study
of healthcare downstream of CPOE alerts. J Am Med Inform Assoc.
2011;18 Suppl 1:i45–50. PMCID: PMC3241165
13. Marshall MS, Boyce RD, Deus H, Zhao J, Willighagen E, Samwald
M, Pichler E, Hajagos J, Prud’hommeaux E, and Stephens, S. Emerging
practices for mapping life sciences data to RDF - a case series. Journal
of Web Semantics. Special Issue: Reasoning with Context in the
Semantic Web. Volume 14, July 2012, Pages 2–13.
14. Open Annotation Collaboration. http://www.openannotation.org/
15. Strom BL, Kimmel SE eds. Textbook of Pharmacoepidemiology. 1st
ed. Wiley; 2007
16. Boyce R, Gardner G, Harkema H. Using Natural Language
Processing to Extract Drug-Drug Interaction Information from Package
Inserts. Proceedings of the 2012 Workshop on BioNLP. Montreal,
Quebec, Canada. June 2012:206-213.
https://www.aclweb.org/anthology/W/W12/W12-2426.pdf

References cont.
17. Rasteger-Mojarad, M., Boyce RD., Prasad, R. UWM-TRIADS:
Classifying Drug-Drug Interactions with Two-Stage SVM and Post-
Processing. Proceedings of the 2013 International Workshop on
Semantic Evaluation (SemEval), Task 9 - Extraction of Drug-drug
Interactions from BioMedical Texts. Atlanta Georgia, June 2013. (In
Press).
18. 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 2013:28-32.
19. 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 2013:64-68.
20. Boyce RD., Handler SM., Karp JF., Perera, S., Hanlon JT. Prevalence
of Potential Drug-Drug Interactions Affecting Antidepressant in US
Nursing Home Residents. Poster presentation at the 2013 Aging
Institute Research Day. University of Pittsburgh. Pittsburgh PA. April,
2013

References cont.
21. E. N. van Roon, S. Flikweert, M. le Comte, P. N. Langendijk, W. J.
Kwee-Zuiderwijk, P. Smits, and J. R. Brouwers. Clinical relevance of
drug-drug interactions : a structured assessment procedure. Drug Saf,
28(12):1131-1139, 2005.
22. 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 Geriatr Pharmacother. 2012 Apr;10(2):139-50. Epub
2012 Jan 27. PMID 22285509. PMCID: PMC3384538
23. Boyce RD, Collins C, Clayton M, Kloke J, Horn J. Inhibitory
Metabolic Drug Interactions with Newer Psychotropic Drugs: Inclusion
in Package Inserts and Influences of Concurrence in Drug Interaction
Screening Software. Annals of Pharmacotherapy. 2012
Oct;46(10):1287-98. Epub 2012 Oct 2. DOI 10.1345/aph.1R150. PMID
23032655
24. http://purl.org/NET/nlprepository/PI-PK-DDI-Corpus
25. http://www.openannotation.org/spec/core/
26. http://purl.org/net/linkedspls

References cont.
27. http://www.fda.gov/OHRMS/DOCKETS/98fr/FDA-2005-N-0464-
gdl.pdf
28. http://goo.gl/C8Vv2
29. http://dailymed.nlm.nih.gov/dailymed/downloadLabels.cfm
30. O. Hassanzadeh et al. “Discovering Linkage Points over Web Data”.
To Appear in PVLDB, Vol 6. Issue 6, August 2013
31. FDA. Guidance for Industry Drug Interaction Studies — Study
Design, Data Analysis, Implications for Dosing, and Labeling
Recommendations. Silver Spring, MD: Federal Drug Administration;
2012. Available at:
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryI
nformation/Guidances/ucm292362.pdf. Accessed January 7, 2013.
32. Platt R, Wilson M, Chan KA, Benner JS, Marchibroda J, McClellan M.
The new Sentinel Network--improving the evidence of medical-product
safety. N Engl J Med. 2009 Aug 13;361(7):645-7.

Backup Slides

Semantic annotation and Linked Data
Product labeling
Data
Semantic annotation
D. Boyce et al. Proceedings of the 2013. AMIA Summit on Translational Bioinformatics. 28-32 [18]
D
D – Semantic annotation of pharmacogenomics statements in
drug product labeling
• First semantically annotated corpus of clinical pharmacogenomics
statements present in drug product labeling
• Potential impact
• pharmacokinetic / pharmacodynamic
• Patient specific risk factors
• concomitant medications
• medical conditions
• Recommendations
• dosage, drug administration, alternatives, monitoring, and tests
• First pharmacogenomics dataset to use the W3C Open Data
Annotation standard [25]

Pharmacogenomics example - Codeine
Predicate Object
drug CODEINE
biomarker CYP2D6
variant Ultra-rapid
metabolizer
Pharmacokinetic
effect
Metabolism-
increase
Pharmacodynamic
effect
Drug-toxicity-risk-
increase
Predicate Object
hasSource URL to product label
Exact-text “Nursing mothers…”
Preceding-
text
…
Post-text …
ex:body-1 ex:target-1
ex:annotation-1
about
“Nursing mothers who are ultra-rapid metabolizers may
also experience overdose symptoms such as extreme
sleepiness, confusion, or shallow breathing.”

Risk factors
patient characteristics X potential adverse event
patient characteristics X DDI mechanism
drug characteristics
route of administration, dose, timing, sequence

Incidence
prevalence of co-prescription
prevalence of AE
incidence of AE in exposed and non-exposed

Seriousness of the AE
Classified by specific clinical outcome
...but, can any seriousness ranking be generally
accepted?
no effect death
?

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

“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….

Starting with all data in the FDA UNII table and DrugBank….
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
“N7U69T4SZR”
UNII
“OLANZAPINE”
Preferred Substance Name
“2-METHYL-4….”
Molecular Formula
“ZYPREXA”
synonym
Idea:
Automatic discovery of
linkage points

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

Addressing gaps in clinically useful evidence on potential drug-drug interactions

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