Evaluating Drug Safety Using Graph Databases

1. © 2022 Neo4j, Inc. All rights reserved.
Evaluating Drug Safety Using
Graph Databases
Zeshan Ghory
Product Director

2. © 2022 Neo4j, Inc. All rights reserved.
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How do we evaluate drug safety?
• By the time drugs reach patients, they
have been through extensive clinical
trials and approved by the relevant
national / regional regulator (e.g. FDA,
EMA etc)
• Even once drugs are approved and on
the market, systems remain in place to
flag up Adverse Events
• Most regulators have their own
spontaneous reporting systems for
Adverse Event logging, the best known
of which is the FAERS system run by
the FDA
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If a drug has adverse events, should we be concerned?
• Most drugs will have some entries in FAERS
◦ How do we know if there’s a potential problem?
◦ Typically use Odds Ratios to analyze the data
BUT…
• FAERS data exhibits bias, because only adverse
events are recorded
◦ “Normal” drug use won’t show up here except incidentally
SO…
• We need to look at other “Real World Evidence” (RWE)
◦ Electronic health records, Insurance claims data etc
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With
Adverse
Event
Without
Adverse
Event
With Drug a b
Without
Drug
c d
ROR =
a/b
c/d

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The coding conundrum – OHDSI and graphs to the rescue!
• Each database encoded using different vocabularies for drugs and conditions
• Fortunately the OHDSI initiative provides (many, but not all) mappings between
many common vocabularies using the OMOP CDM

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Our Approach
Drug Safety
Profiles
The ability to compare FAERS
analysis to Real World
Evidence
FAERS
RWE
Graph (Neo4j)
OHDSI

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Implementation
• Developed a graph model for FAERS data and
populated it in Neo4j
• Loaded ontologies in to Neo4j
• Linked FAERS to ontologies
• RWE sitting in Hadoop cluster
◦ Didn’t make sense to move it in to Neo4j for what we
needed
◦ Graph generates ICD10 (condition) and NDC (drug)
codes corresponding to FAERS data which are passed
to Spark to find matching RWE records
◦ All orchestrated via a Python app
• Built React front-end to turn this in to a SaaS product
which can run the calculations for any drug / adverse
event combination
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Sample Output
• For a given drug and date range as input
◦ Navigate adverse events by MedDRA hierarchy
(showing HLT, but could start even higher up like
SOC)
◦ Pick the specific adverse event of interest
(MedDRA PT)
• Output returns odds ratio calculations for both
FAERS and the RWE data set (in this case
insurance claims data) to allow a comparison
• Having everything connected via the graph also
allows us to do other interesting things
◦ Compare a drug’s safety profile to other drugs in
the same class or the class as a whole (via the
ontologies)
◦ Look at the safety profile of drugs used in
combination
Image Placeholder

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Q & A

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Thank you!
Zeshan Ghory
t: @zeshanghory
e: zeshan.ghory@iqvia.com