Acquiring and representing drug-drug interaction

knowledge and evidence Jodi Schneider

March 29, 2016Litman Lab, CS, University of Pittsburgh

Problem

o Thousands of preventable medication errors occur each year.

o Clinicians rely on information in drug compendia (Physician’s Desk Reference, Medscape, Micromedex, Epocrates, …).

o Compendia have information quality problems:• differ significantly in their coverage, accuracy, and

agreement• often fail to provide essential management

recommendations about prescription drugs

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Prescribers check for known drug interactions.

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Prescribers consult drug interaction references which are maintained by expert pharmacists.

Medscape EpocratesMicromedex 2.0

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Prescribers consult drug interaction references which are maintained by expert pharmacists.

Medscape EpocratesMicromedex 2.0

Significant discrepancies on drug interactions!

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Problem

o Drug Compendia synthesize PDDI evidence into knowledge claims but:• Disagree on whether specific evidence items can

support or refute PDDI knowledge claims

Problem

o Drug Compendia synthesize PDDI evidence into knowledge claims but:• Disagree on whether specific evidence items can

support or refute PDDI knowledge claims• May fail to include important evidence

Silos

Post-market studies

Reported in

Scientific literature

Reported in

Pre-market studies Clinical experience

Drug product labels

Goals

o Long-term, provide drug compendia editors with better information and better tools, to create the information clinicians use.

o This talk focuses on how we might efficiently acquire and represent • knowledge claims about medication safety• and their supporting evidence

o in a standard computable format.

MEDICATION SAFETY DOMAIN

Definitions

o Drug-drug interaction• A biological process that results in a clinically

meaningful change to the response of at least one co-administrated drug.

o Potential drug-drug interaction• POSSIBILITY of a drug-drug interaction• Data from a clinical/physiological study OR

reasonable extrapolation about drug-drug interaction mechanisms

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Existing approaches: RepresentationBradford-Hill criteria (1965)

1. Strength2. Consistency3. Specificity4. Temporality5. Biological gradient6. Plausibility7. Coherence

Bradford-Hill A. The Environment and Disease: Association or Causation?. Proc R Soc Med. 1965;58:295-300.

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Existing approaches: Representation

Horn, J. R., Hansten, P. D., & Chan, L. N. (2007). Proposal for a new tool to evaluate drug interaction cases. Annals of Pharmacotherapy, 41(4), 674-680.

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Existing approaches: RepresentationRoyal Dutch Association for the Advancement of Pharmacy (2005)

1. Existence & quality of evidence on the interaction2. Clinical relevance of the potential adverse

reaction resulting from the interaction3. Risk factors identifying patient, medication or

disease characteristics for which the interaction is of special importance

4. The incidence of the adverse reaction

Van Roon, E.N. et al: Clinical relevance of drug-drug interactions: a structured assessment procedure. Drug Saf. 2005;28(12):1131-9.

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Existing approaches: Representation

Boyce, DIKB, 2006-present 15

Existing approaches: Acquisition

o Evidence

16Boyce, DIKB, circa 2006

DATA MODEL: REPESENTING KNOWLEDGE

Why is a new data model needed?o Need computer integrationo Want a COMPUTABLE model that can make

inferences

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Multiple layers of evidence

Medication Safety Studies

Layer

Clinical Studies and Experiments

Scientific Evidence Layer

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Scientific Evidence Layer: Micropublications

20Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications

MP:Claim

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Building up an MP graph

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Building up an MP graph

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Building up an MP graph

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Building up an MP graph

Building up an MP graph

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Building up an MP graph

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Building up an MP graph

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Building up an MP graph

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Building up an MP graph

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Medication Safety Studies Layer: DIDEO

Brochhausen et al, work in progress, example of Clinical Trial

DIDEO: Drug-drug Interaction and Drug-drug Interaction Evidence Ontology

32https://github.com/DIDEO

EVIDENCE CURATION: ACQUIRING KNOWLEDGE

Hand-extracting knowledge claims and evidence

o Sources• Primary research literature• Case reports• FDA-approved drug labels

o Process• Spreadsheets• PDF annotation

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DIRECTIONS & FUTURE WORK

We are developing a search/retrieval portal It will:o Integrate information (removing silos)o Offer the same information to all compendium

editorso Provide direct access to information

• E.g. quotes in context

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Quotes in context!

Evaluation plan for the search/retrieval portalo 20-person user studyo Measures of

• Completeness of information• Level of agreement• Time required• Perceived ease of use

Implications

o Implications for evidence modeling & curationo Implications for ontology development.o Implications for improving medication safety.

Implications for evidence modeling & curationo Evidence modeling & curation is a general

process.o Analogous processes could be used in other

fields.o Biomedical curation is most mature:

structured nature of the evidence interpretation, existing ontologies, trained curators, information extraction and natural language processing pipelines

o Curation pipelines need to be designed with stakeholders in mind.

Thanks to collaborators & funderso Training grant T15LM007059 from the

National Library of Medicine and the National Institute of Dental and Craniofacial Research

o The entire “Addressing gaps in clinically useful evidence on drug-drug interactions” team from U.S. National Library of Medicine R01 grant (PI, Richard Boyce; R01LM011838) and other collaborators

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“Addressing gaps in clinically useful evidence on drug-drug interactions”

4-year project, U.S. National Library of Medicine R01 grant (PI, Richard Boyce; R01LM011838)o Evidence panel of domain experts: Carol

Collins, Amy Grizzle, Lisa Hines, John R Horn, Phil Empey, Dan Malone

o Informaticists: Jodi Schneider, Harry Hochheiser, Katrina Romagnoli, Samuel Rosko

o Ontologists: Mathias Brochhausen, Bill Hogano Programmers: Yifan Ning, Wen Zhang, Louisa

Zhang

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Jodi Schneider, Mathias Brochhausen, Samuel Rosko, Paolo Ciccarese, William R. Hogan, Daniel Malone, Yifan Ning, Tim Clark and Richard D. Boyce. “Formalizing knowledge and evidence about potential drug-drug interactions.” International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I 2015) at ISWC 2015 Bethlehem, Pennsylvania, USA.

Jodi Schneider, Paolo Ciccarese, Tim Clark and Richard D. Boyce. “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” 4th Workshop on Linked Science 2014—Making Sense Out of Data (LISC2014) at ISWC 2014 Riva de Garda, Italy.

Mathias Brochhausen, Jodi Schneider, Daniel Malone, Philip E. Empey, William R. Hogan and Richard D. Boyce “Towards a foundational representation of potential drug-drug interaction knowledge.” First International Workshop on Drug Interaction Knowledge Representation (DIKR-2014) at the International Conference on Biomedical Ontologies (ICBO 2014) Houston, Texas, USA.

Richard D. Boyce, John Horn, Oktie Hassanzadeh, Anita de Waard, Jodi Schneider, Joanne S. Luciano, Majid Rastegar-Mojarad, Maria Liakata, “Dynamic Enhancement of Drug Product Labels to Support Drug Safety, Efficacy, and Effectiveness.” Journal of Biomedical Semantics. 4(5), 2013. doi:10.1186/2041-1480-4-5

o Evidence

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7.19 Drugs Metabolized by Cytochrome P4502D6In vitro studies did not reveal an inhibitory effect of escitalopram on CYP2D6.