Inadequate representation of evidence and knowledge about potential drug-drug interactions is a major factor underlying disagreements among sources of drug information that are used by clinicians. In this paper we describe the initial steps toward developing a foundational domain representation that allows tracing the evidence underlying potential drug-drug interaction knowledge. The new representation includes biological and biomedical entities represented in existing ontologies and terminologies to foster integration of data from relevant fields such as physiology, anatomy, and laboratory sciences.

1. Towards a foundational
representation of potential drug-drug
interaction knowledge
M Brochhausen, J Schneider, D Malone, PE Empey, WR
Hogan, RD Boyce

2. "Addressing gaps in clinically useful
evidence on drug-drug interactions"
Goal:
• Identify the core components of a new knowledge
representation paradigm that enables better
integration of drug-drug interaction evidence
– Does the interaction exist?
– What potential impact?
– How to best manage exposure
Hypothesis:
• The new paradigm will result in more complete,
accurate, and current knowledge available for
decision support

3. Important distinctions
• Potential drug-drug interactions (PDDIs) ≠
Drug-drug interactions
– A drug-drug interaction is an interaction between
two or more drugs in a actual patient
• i.e., some real clinical effect harmful or helpful
– A PDDI involves two or more drugs for which there
exists a known interaction potential
• i.e., only the potential exists for some real clinical effect
• A manageable source of drug-related adverse events

4. Clinically relevant PDDIs
Van Roon et al. Drug Saf. Int. J. Med. Toxicol. Drug Exp. 28, 1131–1139 (2005).

5. Three contexts of PDDI knowledge
representation
 Evidence base:
 evidence for and against
 patient risk factors,
 seriousness of an adverse event
 incidence
 Knowledge base:
 PDDI assertions
 pharmacologic/physiological assertions
 Reasoning system:
 ensuring logically consistency of inferred knowledge
with all existing assertions in the knowledge base.

6. Informational dependencies between
Evidence base, Knowledge base and
Reasoning system

7. Motivation
The development of our new semantic resource
is motivated by the needs of experts who must
search, evaluate, and synthesize PDDI evidence
into knowledge claims.

8. Requirements
 Linkage between the semantic model of the
evidence base and the knowledge base.
 Represent PDDI evidence without inferring the
existence of an actual drug-drug interaction.
 Not all PDDIs are actualized.
 Represent drug entities according to the scientific
state-of-the-art of pharmacology.
 Semantic schema of knowledge base represented in
a coding language that allows consistency check.

9. Potential Drug-Drug Interaction
Definition:
"A potential drug-drug interaction (PDDI) is an
information content entity that specifies the
possibility of a drug-drug interaction based on
either reasonable extrapolation about drug-drug
interaction mechanisms or a data item
created by clinical studies, clinical observation
or physiological experiments."

10. Related work
 Drug Interaction Ontology (DIO) [1]
 Drug-Drug Interaction Ontology (DINTO) [2]
1 Arikuma et al. BMC Bioinformatics. 9, S11, 2008.
2 Herrero-Zazo et al. http://ceur-ws.org/Vol-1114/Session3_Herrero-Zazo.pdf,
2013.

11. Drug Interaction Ontology (DIO) 1/2
 developed with the goal of predicting drug
interactions
 inspired by both Basic Formal Ontology (BFO)
and the NCI Thesaurus (via UMLS)
– it is not aligned with either one

12. Drug Interaction Ontology (DIO) 2/2
 No distinction between drug products, their
ingredients and the molecules that constitute those
ingredients.
 Each instance of a chemical is a drug, regardless
of dosage or formulation
 potential to assign incorrect properties
 NDF-RT asserts that vancomycin capsules
"may treat" bacterial endocarditis and
pneumococcal meningitis. However, only
intravenous vancomycin can treat those
conditions. [1]
1 Hogan WR et al.
http://www2.unb.ca/csas/data/ws/icbo2013/papers/research/icbo2013_submission_40
.pdf, 2013.

13. Drug Interaction Ontology (DINTO) 1/2
DINTO is intended "to represent all possible
mechanisms that can lead to a drug-drug
interaction. The ontology provides the general
pharmacological principles of the domain"[1].
1 Herrero-Zazo et al. http://ceur-ws.org/Vol-1114/Session3_Herrero-Zazo.pdf,
2013.

14. Drug Interaction Ontology (DINTO) 2/2
 DINTO represents drug-drug interactions, not
PDDIs.
 However, DINTO specifies a subclass of DDIs
named DDI described in a database.
 DDI in database represents "those DDIs imported
in DINTO from the DrugBank database with the
purpose of distinguishing them from those
inferred from the ontology”.
 Yet, DrugBank contains PDDI information.

15. DIDEO: Methods (based on OBO
Foundry principles)
 Reuse of pre-existing ontologies
 Basic Formal Ontology
 http://purl.obolibrary.org/obo/bfo.owl
 Drug Ontology
 http://purl.obolibrary.org/obo/dron.owl
 Ontology of Biomedical Investigations
 http://purl.obolibrary.org/obo/obi.owl
 Gene Ontology
 http://purl.obolibrary.org/obo/go.owl
 Information Artifact Ontology
 http://purl.obolibrary.org/obo/iao.owl
 Chemical Entities of Biological Interest
 http://www.ebi.ac.uk/chebi/
 Intended to be open source and community-driven

16. Four informational bases of PDDIs
1) Reasonable extrapolation
2) Physiological observations from clinical
studies
3) Drug-drug interaction observational data
4) Mechanistic assertions that are useful for
inferring drug-drug interactions

17. Four informational bases of PDDIs

18. Physiological experiment
The data establishes the
existence of a disposition borne
The drug metabolism
becomes part of an assay.
by the enzyme.
Substances
come in
portion!

19. Next steps
 Creating github project and an owl file for v1
of DIDEO (under development).
 coordinate with DINTO and DRON
 integrate what we learn from today's
workshop
 Iterative improvement of the representation
larger number of PDDI instances get
integrated (next 12 month)
 Coordinate with other stakeholders (NLM,
FDA, NDF-RT, Cochrane Collaboration, W3C
HCLS)

20. Acknowledgements
 For all authors: This project is supported by a grant from the National
Library of Medicine: “Addressing gaps in clinically useful evidence on drug-drug
interactions” (R01LM011838-01) and the National Institute of Aging
“Improving medication safety for nursing home residents prescribed
psychotropic drugs” (K01 AG044433-01).
 The authors thank Michel Dumontier and Alan Ruttenberg for their
valuable comments, which have significantly improved the paper.
 For PE: This work is supported by the National Center For Advancing
Translational Sciences of the National Institutes of Health under Award
Number KL2TR000146.
 For DM: This work is partially supported by the Agency for Healthcare
Research and Quality (AHRQ) Grant No. 1R13HS021826-01 (Malone DC-PI)
 For JS: This work was carried out during the tenure of an ERCIM “Alain
Bensoussan” Fellowship Programme. The research leading to these results
has received funding from the European Union Seventh Framework
Programme (FP7/2007-2013) under grant agreement no 246016.