A poster for the National Library of Medicine Informatics Training Conference 2016: https://u.osu.edu/nlm2016/conference-agenda/ Potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm. Poor quality evidence on PDDIs, combined with prescribers’ general lack of PDDI knowledge, results in thousands of preventable medication errors each year. One contributing factor is that PDDI knowledge lacks a standard computable format. To address this, we are researching efficient strategies for acquiring and representing PDDIs knowledge, focusing on assertions and their supporting evidence. We are acquiring knowledge from several sources. First, we have transformed 410 assertions and 519 evidence items from prior work. Second, we are examining FDA-approved drug labels, and so far annotators have identified 609 evidence items relating to pharmacokinetic PDDIs from 27 FDA-approved drug labels. Third, annotators have found 230 assertions of drug-drug interactions in 158 non-regulatory documents, including full text research articles. We are building a two-layer evidence representation, with both generic and domain-specific layers. The generic layer reuses the Micropublications Ontology to annotate assertions and their supporting data, methods, and materials. For the domain-specific component we are building DIDEO–the Drug-drug Interaction and Drug-drug Interaction Evidence Ontology. DIDEO adds specific knowledge, such as the study types required to establish a given type of PDDI. The current version of DIDEO has 385 subclass axioms, and reuses formalized knowledge items, including from the Drug Ontology, Chemical Entities of Biological Interest, the Ontology of Biomedical Investigations, and the Gene Ontology.

1. Jodi Schneider & Richard D. Boyce
Department of Biomedical Informatics
School of Medicine, University of Pittsburgh
Designing
a
data
model
for
represen1ng
claims
and
evidence
1. Iden1fy
key
ontologies
relevant
for
claims
and
evidence.
• Nanopublica+ons
Ontology
represents
se4led
science:
Each
formalized
claim
(asser+on)
is
wrapped
in
provenance
and
publica+on
info.
• Micropublica+ons
Ontology
represents
claims
and
evidence.
It
views
a
scien+fic
paper
as
a
network
of
claims
supported
by
data,
methods
and
materials.
Claims,
data,
methods,
and
materials
can
be
text,
images,
or
mul+media:
anything
the
Open
Annota+on
Ontology
can
reference.
2. Iden1fy
key
domain
ontologies
to
reuse.
• Ontology
of
Biomedical
Inves+ga+ons
• Chemical
En++es
of
Biological
Interest
• Drug
Ontology
3.
Conceptualize
3
layers
and
determine
what
belongs
in
each
layer:
4.
Formalize
key
terms
about
drug-­‐drug
interac1ons
in
a
new
ontology,
DIDEO.
For
instance,
“poten+al
drug-­‐drug
interac+on”
gets
obo:DIDEO_00000000
5.
Create
micropublica1ons
claims
and
nanopublica1on
asser1ons.
Micropublica+on
M:
“Clarithromycin
interacts
with
simvasta+n”
Nanopublica+on
asser+on
N:
obo:CHEBI_3732
obo:DIDEO_00000000
obo:
CHEBI_9150
6.
Connect
natural
language
quota1ons
and
their
informa1on-­‐retrieval
friendly
versions.
M
mp:formalizedBy
N
.
N
mp:formalizes
M
.
Ontologies,
Data,
and
Websites
DIDEO:
The
Poten+al
Drug-­‐drug
Interac+on
and
Poten+al
Drug-­‐drug
Interac+on
Evidence
Ontology
h4ps://github.com/DIDEO/DIDEO
Contribu+ons
to
Micropublica+ons
Ontology
(formalizes/formalizedBy):
h4ps://github.com/dbmi-­‐pi4/DIKB-­‐Micropublica+on/blob/master/data/
mp_1.18.owl
Drug
Interac+on
Knowledge
Base
website
&
discussion
forums
h4p://dikb.org
and
h4p://forums.dikb.org
Problem
Poten+al
drug-­‐drug
interac+ons
are
a
significant
source
of
preventable
drug-­‐related
harm.
The
drug
informa+on
sources
clinicians
use
are
disconcordant:
Most
drug
informa1on
sources
disagree
substan1ally
in
their
content
(e.g.
Abarca
et
al.
2003,
Wang
et
al.
2010,
Saverno
et
al.
2011).
This
problem
has
persisted
for
more
than
a
decade
(e.g.
Ayvaz
et
al.
2015,
Ekstein
et
al.
2015)
despite
extensive
editorial
work
on
the
part
of
each
drug
informa+on
source.
This
is
in
part
because:
(1) There
is
no
standard,
agreed
upon
method
for
assessing
evidence
about
drug-­‐drug
interac+ons.
(2) Knowledge
claims
and
evidence
about
drug-­‐drug
interac+ons
are
distributed
across
mul+ple
sources:
pre-­‐market
studies,
post-­‐market
studies,
and
clinical
experience.
University
of
PiPsburgh
Department
of
Biomedical
Informa1cs
Funded
by
training
grant
T15LM007059
from
the
Na1onal
Library
of
Medicine/Na1onal
Ins1tute
of
Dental
and
Craniofacial
Research
and
by
R01LM011838
from
the
Na1onal
Library
of
Medicine.
Approach
Annota1on
Results
From
prior
work,
we
have
transformed
• 410
asser+ons
• 519
evidence
items
Annotators
have
also
iden+fied
evidence
in
• 158
non-­‐regulatory
documents
(including
full-­‐text
research
ar+cles)
• 27
FDA-­‐approved
drug
labels
This
leads
to
an
addi+onal:
• 230
asser+ons
of
drug-­‐drug
interac+ons
in
non-­‐regulatory
documents
• 609
evidence
items
rela+ng
to
poten+al
pharmacokine+c
drug-­‐drug
interac+ons
from
27
FDA-­‐approved
drug
labels
Acquiring
claims
and
evidence
1.
Formulate
claims
of
interest.
“Clarithromycin
interacts
with
simvasta+n”.
2.
Iden1fy
relevant
source
documents.
Source
documents
include
FDA-­‐approved
drug
product
labels
and
full-­‐text
research
papers
(clinical
trials
and
case
reports).
3.
Experts
assess
quality
&
relevance
of
source
documents.
Experts
check
that
documents
meet
inclusion
criteria.
Experts
find
relevant
claims,
methods,
and
results.
4.
Pre-­‐annota1on
by
computer
text
mining.
Source
documents
are
pre-­‐processed
to
find
drug
men+ons,
using
named
en+ty
recogni+on
algorithms.
5.
Human
curators
annotate
full-­‐text
documents.
(a) The
curator
highlights
the
claim.
(b) The
curator
enters
the
claim
and
scien+fic
method.
(c) The
curator
is
prompted
to
add
data
based
on
the
method.
Claim 1
[Clarithryomycin
interacts with
Simvastatin]
Data 1
Micropublication 1
mp:argues
Method 1
mp:qualifies
obo:CHEBI_3732
[Clarithryomycin]
mp:qualifies
obo:CHEBI_9150
[Simvastatin]
mp:qualifies
obo:DIDEO_00000000
[Potential drug-drug
interaction]
Materials 1
mp:supports
mp:supports
mp:supports
mp:supports
doi:
"Clarithryomycin significantly (p<0.001)
increased the AUC (and Cmax) of all 3
statins, most markedly simvastatin"
oa:hasSource
Publica1ons
and
Presenta1ons
1.
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
poten+al
drug-­‐drug
interac+ons.”
Interna7onal
Workshop
on
Biomedical
Data
Mining,
Modeling,
and
Seman7c
Integra7on
at
Interna7onal
Seman7c
Web
Conference
2015
h4p://ceur-­‐ws.org/Vol-­‐1428/BDM2I_2015_paper_10.pdf
2.
Jodi
Schneider,
Paolo
Ciccarese,
Tim
Clark
and
Richard
D.
Boyce.
“Using
the
Micropublica+ons
ontology
and
the
Open
Annota+on
Data
Model
to
represent
evidence
within
a
drug-­‐drug
interac+on
knowledge
base.”
4th
Workshop
on
Linked
Science
at
Interna7onal
Seman7c
Web
Conference
2014
h4p://ceur-­‐ws.org/Vol-­‐1282/lisc2014_submission_8.pdf
3.
Mathias
Brochhausen,
Jodi
Schneider,
Daniel
Malone,
Philip
E.
Empey,
William
R.
Hogan
and
Richard
D.
Boyce
“Towards
a
founda+onal
representa+on
of
poten+al
drug-­‐drug
interac+on
knowledge.”
First
Interna7onal
Workshop
on
Drug
Interac7on
Knowledge
Representa7on
at
the
Interna7onal
Conference
on
Biomedical
Ontologies
2014
h4p://ceur-­‐ws.org/Vol-­‐1309/paper2.pdf
4.
Mathias
Brochhausen,
Philip
E.
Empey,
Jodi
Schneider,
William
R.
Hogan,
and
Richard
D.
Boyce.
Adding
evidence
type
representa+on
to
DIDEO.
ICBO
2016
h4p://jodischneider.com/pubs/icbo2016.pdf
5.
Jodi
Schneider,
Samuel
Rosko,
Yifan
Ning,
and
Richard
D.
Boyce.
“Towards
structured
publishing
of
poten+al
drug-­‐drug
interac+on
knowledge
and
evidence.
Poster
presenta+on
at:
the
Pi4sburgh
Biomedical
Informa+cs
Training
Program
2015
Retreat.
Pi4sburgh,
PA,
August
20,
2015.
doi:10.6084/m9.figshare.1514991
6.
Jodi
Schneider
and
Richard
D.
Boyce
“Medica+on
safety
as
a
use
case
for
argumenta+on
mining”.
Dagstuhl
Seminar
16161:
Natural
Language
Argumenta+on:
Mining,
Processing,
and
Reasoning
over
Textual
Arguments,
Dagstuhl,
Germany,
April
19,
2016
h4p://www.slideshare.net/jodischneider/medica+on-­‐safety-­‐as-­‐a-­‐use-­‐case-­‐for-­‐
argumenta+on-­‐mining-­‐dagstuhl-­‐seminar-­‐16161-­‐2016-­‐0419
Future
Work
• Build
an
informa+on
portal
that
supports
clinical
pharmacists
and
drug
informa+on
professionals
in
retrieving
the
claims
and
evidence.
• Test
the
informa+on
portal
in
a
task-­‐based,
within-­‐subject,
user
study.
Measure
the
completeness
of
the
informa+on
experts
retrieve
with
our
informa+on
portal
compared
to
current
state-­‐of-­‐the-­‐art
retrieval
tools.
• Test
the
feasibility
of
authors
annota+ng
their
own
claims
and
evidence.
• Enable
annota+on
beyond
PubMed
Central
open
access
HTML.
• Use
rulesets
of
“belief
criteria”
to
transform
evidence
to
a
knowledge
base.
Aim
In
this
work,
we
address
the
distributed
nature
of
drug-­‐drug
interac+on
knowledge,
by
developing
a
computable
representa+on
for
claims
and
evidence
about
drug-­‐drug
interac+ons.
Our
goal
is
to
support:
(a) Knowledge
acquisi+on
from
full-­‐text
natural
language
(b) Search
and
retrieval
of
all
evidence.
We
are
applying
this
representa+on
to
acquire
claims
and
evidence
about
pharmacokine+c
interac+ons
for
65
drugs.
This
will
help
us
design
a
search
portal,
to
test
whether
computable
representa+ons
of
knowledge
claims
and
evidence
can
improve
search
and
retrieval
of
poten+al
drug-­‐
drug
interac+ons.
Longer
term,
we
will
test
whether
this
can
help
reduce
the
disconcordance
between
different
drug
informa+on
sources.
We
model
knowledge
as
claims
supported
by
evidence.
asser+on
provenance
publica+on
info
nanopublica+on
DIDEO:
formalizing
medica1on
safety
studies
As
an
OWL
ontology,
DIDEO
supports
querying
as
well
as
reasoning.
For
instance,
curators
will
only
need
to
enter
a
few
facts
in
order
for
the
scien+fic
method
of
a
study
to
be
automa+cally
determined.
DIDEO
uses
ontological
realism
to
dis+nguish:
• Poten+al
vs.
actual
drug-­‐drug
interac+ons.
• Inferred
vs.
observed
interac+ons.
Method
Data
Claim
Dosage,
regimen,
clearance,
Cmax,
AUC,
half
life
Number
of
par+cipants,
randomiza+on
Claim,
data,
method,
and
material