Limitations in the information available to clinicians are a contributing factor to the many thousands of preventable medication errors that occur each year. Current knowledge sources about potential drug-drug interactions (PDDIs) often fail to provide essential management recommendations and differ significantly in their coverage, accuracy, and agreement. To address this, we seek to more efficiently acquire and represent PDDIs knowledge claims and their supporting evidence in a standard computable format.   In this talk we will present work in progress on both representation (a data model) and acquisition (an evidence curation pipeline). Our data model has a reusable generic layer, provided by the Micropublications Ontology, as well as a domain-specific layer represented using the new Drug-drug Interaction and Drug-drug Interaction Evidence Ontology (DIDEO). We will discuss the motivation for our approach and possible implications for representing evidence from other biomedical domains. On the curation side, we will describe how our research team is hand-extracting knowledge claims and evidence from the primary research literature, case reports, and FDA-approved drug labels. This work has implications for ontology development, the design of curation pipelines, and for improving medication safety. Talk for the RWTH Aachen, Fachgruppe Informatik - Knowledge-based Systems Group, Aachen, Germany, April 25, 2016.

1. Acquiring and representing drug-
drug interaction
knowledge and evidence​
Jodi Schneider and Richard D. Boyce
RWTH Aachen
Fachgruppe Informatik - Knowledge-based Systems Group
Aachen, DE
2016-04-25
1

2. MEDICATION SAFETY
2

3. Prescribers check for known drug interactions.
3

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

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

6. 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
6

7. Problem
o Drug compendia synthesize drug interaction
evidence into knowledge claims but:
• Disagree on whether specific evidence items can support
or refute particular knowledge claims
7

8. Problem
o Drug compendia synthesize drug interaction
evidence into knowledge claims but:
• Disagree on whether specific evidence items can support
or refute particular knowledge claims
• May fail to include important evidence
8

9. Silos: Multiple sources of information
Post-market studies
Reported in
Scientific literature
Pre-market studies Clinical experience
Drug product labels
(US Food and Drug
Administration)
Reported in
9

10. “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
10

11. 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
• In a standard computable format.
11

12. MEDICATION SAFETY DOMAIN
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13. 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.]
13

14. 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.]
14

15. Existing approaches: Representation
1. Are there previous credible reports in humans?
• If there are case reports or prospective studies that clearly provide evidence
supporting the interaction, answer YES. For case reports, at least one case
should have a “possible” DIPS rating (score of 2 or higher).
• If a study appropriately designed to test for the interaction shows no
evidence of an interaction, answer NO.
…
5. Did the interaction remit upon de-challenge of the
precipitant drug with no change in the object drug? (if no
de-challenge, use Unknown or NA and skip Question 6)
• Stopping the precipitant drug should bring about resolution of the
interaction, even if the object drug is continued without change. …
• If dechallenge of the precipitant drug without a change in object drug did not
result in remission of the interaction, answer NO.
• If no dechallenge occurred, the doses of both drugs were altered, or no
information on dechallenge is provided, answer NA.
[Horn et al. 2007] 15

16. 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.]
16

17. Existing approaches: Representation
Boyce, DIKB, 2006-present 17

18. Existing approaches: Acquisition
o Evidence
18Boyce, DIKB, circa 2006

19. EXAMPLE
19

20. 20

21. [Hu et al. 2011] 21

22. 22[Hu et al. 2011]

23. 23[Hu et al. 2011]

24. 24[Hu et al. 2011]

25. [Boyce, DIKB, 2006-present] 25

26. [Boyce, DIKB, 2006-present] 26

27. DESIGNING AN EVIDENCE
BASE
27

28. Multiple layers of evidence
Medication Safety
Studies Layer
Clinical Studies and
Experiments
Scientific Evidence Layer
28

29. [Brochhausen, Schneider, Malone, Empey, Hogan and Boyce “Towards a foundational representation of
potential drug-drug interaction knowledge.” First International Workshop on Drug Interaction
Knowledge Representation (DIKR-2014) at ICBO.]
29

30. SCIENTIFIC EVIDENCE LAYER
30

31. Scientific Evidence Layer: Micropublications
[Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and
annotations in biomedical communications.]
31

32. Scientific Evidence Layer: Micropublications
[Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and
annotations in biomedical communications] 32

33. MODELING NARRATIVE
DOCUMENTS AS EVIDENCE
33

34. 34

35. 35

36. MP:Claim
36

37. 37

38. Building up an MP graph
38

39. Building up an MP graph
39

40. Building up an MP graph
40

41. Building up an MP graph
41

42. Building up an MP graph
42

43. Building up an MP graph
43

44. Building up an MP graph
44

45. Building up an MP graph
45

46. Building up an MP graph
46

47. Medication Safety Studies Layer:
DIDEO
Brochhausen et al, work in progress, example of Clinical Trial
47

48. DIDEO: Drug-drug Interaction and Drug-
drug Interaction Evidence Ontology
https://github.com/DIDEO 48

49. 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
49

50. HAND ANNOTATION TO
CREATE THE EVIDENCE BASE
50

51. Hand-extracting claims and evidence
o Sources
• Primary research literature
• Case reports
• FDA-approved drug labels
o Process
• Spreadsheets
• PDF annotation
51

52. 52

53. 53

54. Work to date
o 410 assertions and 519 evidence items transformed
from prior work.
o 609 evidence items (pharmacokinetic potential
drug-drug interactions) annotated by hand from 27
FDA-approved drug labels.
o 230 assertions of drug-drug interactions annotated
by hand from 158 non-regulatory documents,
including full text research articles.
54

55. DIRECTIONS & FUTURE WORK
55

56. We are developing a search/retrieval portal
It will:
o Integrate across multiple types of source materials
(FDA drug labels, scientific literature, …)
o Systematize search: Enable ALL drug compendium
editors to access the same info
o Provide direct access to source materials
• E.g. quotes in context
56

57. 57

58. 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
58

59. Generate multiple KBs from the same EB
59

60. Evidence modeling & curation
o Analogous processes could be used in other fields:
evidence modeling & curation is a general process.
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.
60

61. 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
61

62. 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 62

64. Other Implications
o Implications for ontology development.
o Implications for improving medication safety.
64

65. o What arguments are used in medication safety?
o How can these arguments be mined/identified?
o What work needs to be done?
65

66. Why is a new data model needed?
o Need computer integration
o Want a COMPUTABLE model that can make
inferences
66