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

Biomedical Informatics1

Addressing gaps in clinically useful evidence on drug-drug interactionsMay 2nd 2013 BioDLP Seminar at the University of Wisconsin - Milwaukee

Richard Boyce, University of PittsburghDepartment 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 pharmacodynamic


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 inRarely 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 inReported in

Rarely reported in


Source forSource 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 paradigmProduct 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

AnnHathawayShakespeare

Hamlet

marriedwrote


Semantic Annotation

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

Semantic Annotation of PDDIs

Combining Linked Data and Semantic Annotation


A structured assessment scores evidence and potential severity [21]

Pharmacoepidemiology – filling in the gaps

Recap of the new paradigmProduct labeling




Semantic annotation


Semantic annotation

Reduced risk of a PDDI

medication error!

Clinical experience


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 labelingScientific literature



Semantic annotation


Semantic annotation

Clinical experience


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


C. Boyce et al. Proceedings of the 2012. Workshop on BioNLP. 206-213 [16] 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 labelsProduct labeling

C

C. Boyce et al. Proceedings of the 2012. Workshop on BioNLP. 206-213 [16]

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 DrugBank1. 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-labels2. http://thedatahub.org/dataset/linkedct3. http://bioportal.bioontology.org/ontologies/471014. 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


References1. 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. 2011;18(1):32–37.


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; 200716. 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/GuidanceComplianceRegulatoryInformation/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


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 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


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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