Presentation to Diane Litman's lab at the University of Pittsburgh about modeling and acquiring evidence for the Drug Interaction Knowledge Base (DIKB) project.

1. Acquiring and representing drug-
drug interaction
knowledge and evidence​
Jodi Schneider
March 29, 2016
Litman Lab, CS, University of Pittsburgh

2. 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
2

3. Prescribers check for known drug interactions.
3

4. Prescribers consult drug interaction references
which are maintained by expert pharmacists.
Medscape EpocratesMicromedex 2.0
4

5. Prescribers consult drug interaction references
which are maintained by expert pharmacists.
Medscape EpocratesMicromedex 2.0
5

6. Problem
o Drug Compendia synthesize PDDI evidence into
knowledge claims but:
• Disagree on whether specific evidence items can support
or refute PDDI knowledge claims

7. 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

8. Silos
Post-market studies
Reported in
Scientific literature
Reported in
Pre-market studies Clinical experience
Drug product labels

9. 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.

10. MEDICATION SAFETY DOMAIN

11. 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
11

12. Existing approaches: Representation
Bradford-Hill criteria (1965)
1. Strength
2. Consistency
3. Specificity
4. Temporality
5. Biological gradient
6. Plausibility
7. Coherence
Bradford-Hill A. The Environment and Disease: Association or Causation?.
Proc R Soc Med. 1965;58:295-300.
12

13. 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.
13

14. Existing approaches: Representation
Royal Dutch Association for the Advancement of
Pharmacy (2005)
1. Existence & quality of evidence on the interaction
2. Clinical relevance of the potential adverse reaction
resulting from the interaction
3. 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.
14

15. Existing approaches: Representation
Boyce, DIKB, 2006-present 15

16. Existing approaches: Acquisition
o Evidence
16Boyce, DIKB, circa 2006

17. DATA MODEL: REPESENTING
KNOWLEDGE

18. Why is a new data model needed?
o Need computer integration
o Want a COMPUTABLE model that can make
inferences
18

19. Multiple layers of evidence
Medication Safety
Studies Layer
Clinical Studies and
Experiments
Scientific Evidence Layer
19

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

21. MP:Claim
21

22. Building up an MP graph
22

23. Building up an MP graph
23

24. Building up an MP graph
24

25. Building up an MP graph

26. Building up an MP graph
26

27. Building up an MP graph
27

28. Building up an MP graph
28

29. Building up an MP graph
29

30. Building up an MP graph
30

31. Medication Safety Studies Layer:
DIDEO
Brochhausen et al, work in progress, example of Clinical Trial

32. DIDEO: Drug-drug Interaction and Drug-
drug Interaction Evidence Ontology
32https://github.com/DIDEO

33. EVIDENCE CURATION:
ACQUIRING KNOWLEDGE

34. Hand-extracting knowledge claims and
evidence
o Sources
• Primary research literature
• Case reports
• FDA-approved drug labels
o Process
• Spreadsheets
• PDF annotation
34

35. 35

36. 36

37. 37

38. DIRECTIONS & FUTURE WORK

39. We are developing a search/retrieval portal
It will:
o Integrate information (removing silos)
o Offer the same information to all compendium
editors
o Provide direct access to information
• E.g. quotes in context

40. 40

41. Evaluation plan for the search/retrieval portal
o 20-person user study
o Measures of
• Completeness of information
• Level of agreement
• Time required
• Perceived ease of use

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

43. Implications for evidence modeling &
curation
o 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.

44. Thanks to collaborators & funders
o 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
44

45. “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 Hogan
o Programmers: Yifan Ning, Wen Zhang, Louisa
Zhang
45

46. 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

48. o Evidence
48
7.19 Drugs Metabolized by Cytochrome P4502D6
In vitro studies did not reveal an inhibitory effect of
escitalopram on CYP2D6.