A talk to developers of The Ontology for Biomedical Investigations (OBI), in the lead-up to the OBI-ECO workshop in Baltimore May 11th and 12th. Focus is the evidence modeling underway for the "Addressing gaps in clinically useful evidence on drug-drug interactions" R01 project led by Richard Boyce. See also https://github.com/dbmi-pitt/DIKB-Micropublication

1. Evidence and knowledge on
drug-drug interactions to support
drug compendia editors
Jodi Schneider
1
OBI-DEV call
2016-05-09

2. “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
2

3. 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, …).
3

4. Problem
o Drug compendia have information quality problems:
• differ significantly in their coverage, accuracy, and
agreement
• often fail to provide essential management
recommendations about prescription drugs
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
4

5. Goals
o Long-term, provide drug compendia editors with
better information and better tools, to create the
information clinicians use.
o Work on semantic representation and annotation:
• Representing knowledge claims about medication safety
• Representing their supporting evidence
• Acquiring knowledge claims & evidence
5

6. DOMAIN-SPECIFIC
ARGUMENTATION
6

7. Drug Interaction Probability Score
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] 7

8. [Boyce, DIKB, 2006-present] 8

9. [Boyce, DIKB, 2006-present] 9

10. DESIGNING AN EVIDENCE
BASE
10

11. Multiple layers of evidence
Medication Safety
Studies Layer
Clinical Studies and
Experiments
Scientific Evidence Layer
11

12. [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.]
12

13. SCIENTIFIC EVIDENCE LAYER
13

14. Scientific Evidence Layer: Micropublications
[Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and
annotations in biomedical communications.]
14

15. Scientific Evidence Layer: Micropublications
[Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and
annotations in biomedical communications] 15

16. MODELING NARRATIVE
DOCUMENTS AS EVIDENCE
16

17. 17

18. 18

19. MP:Claim
19

20. 20

21. Building up an MP graph
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
25

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. 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.
30

31. DIRECTIONS & FUTURE WORK
31

32. 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
32

33. 33

34. 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
34

35. Generate multiple KBs from the same EB
35

36. Evidence Base
"erythromycin inhibits CYP3A4."
"CYP3A4 catalyzes a Phase I or
Phase II enzymatic reaction
involving simvastatin."
OWL
inference
"erythromycin
‘inhibits-catalyzes metabolism of’
simvastatin."
Knowledge Base
}
Statement A
"Statement A is about
{erythromycin}."
"Statement A is about
{simvastatin}."
"Statement A is about {CYP3A4}."
"Statement A is a PDDI statement."
transformation
Each OWL-inferred assertion in the evidence
base generates a new individual in the
knowledge base.

37. 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
37

38. 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 38