Elsevier has significantly reduced the cost of drug development for rare diseases through drug repurposing. They have brought together knowledge about drug targets, effects, and disease biology to identify drugs and nutraceuticals approved by the FDA that could potentially be repositioned to treat rare diseases, eliminating the need for new drug development and clinical trials. Using automated queries of Elsevier knowledgebases, they can provide summaries of potential treatments for a given rare disease, including key researchers and institutions, relevant drug targets, and approved drugs that may be effective - reducing the cost of repositioning existing drugs to under $500,000.

1. Anton Yuryev, PhD, Elsevier Professional Services
a.yuryev@elsevier.com
November 1, 2017
Mobilizing informational resources
for rare diseases
When every piece matters

2. | 2
Elsevier significantly reduced the price of drug development for rare diseases:
• We brought together knowledgebase about drug targets, drug effects and
disease biology
• We find many drugs from 2,365 approved by FDA as well as nutraceuticals
can be repositioned for treatment of rare diseases eliminating a need
 Drug development
 Clinical trials
• We automated the drug repurposing by designing automatic queries to
Elsevier knowledgebases
• We bring together rare disease experts, centers of medical excellence and
patient communities
• The cost of repositioning existing FDA approved drug for rare disease is now
under $500,000. This level of funding can be obtained through:
 government grants
 angel investment
 patient organization funds
 charities
Key message: Rare disease are no longer orphan
Drug repurposing for rare diseases

3. | 3
• A rare genetic disease
• Permanently excessive level of
insulin in the blood
• Develops within the first few days of life
Symptoms include floppiness, shakiness, poor
feedings, seizures, fits and convulsions.
 If not caught quickly can lead to brain
injury or even death.
 In the most severe cases the only viable
treatment is the removal of the
pancreas, consigning the patient to a
lifetime of diabetes.
Congenital hyperinsulinsm
is a UK charity that is building the
rare disease community to raise
awareness, drive research and
develop treatments through
patient empowerment.
is partnering
with Findacure scientists to help
identify and evaluate treatments
for this devastating disease.

4. | 4
Congenital hyperinsulinism library
Education and knowledge sharing between patients, doctors, health professionals
• Collection of >1,200 papers sorted by disease and study type
• Access to all Elsevier’s full-text publications covering rare disease in ScienceDirect

5. | 5
Why do we need literature?
PLACES
PEOPLE
GENES
DRUGS
INTERACTIONSPROPERTIES

6. | 6
The power of processed content
PEOPLE
GENES
DRUGS
INTERACTIONSPROPERTIES
DATA NORMALIZATION DATABASES TOOLS
PLACES

7. | 8
Research landscape analysis: connecting patients,
researchers and institutions
0 10 20 30 40 50 60 70
Stanley, C.A.
Hussain, K.
De Lonlay, P.
Rahier, J.
Ellard, S.
Flanagan, S.E.
Shyng, S.L.
Nihoul-Fekete, C.
Bellanne-Chantelot, C.
Robert, J.J.
Brunelle, F.
KEY AUTHORS
0 10 20 30 40 50 60 70 80
The Children's Hospital of Philadelphia
UCL Institute of Child Health
Hopital Necker Enfants Malades
University of Pennsylvania, School of…
UCL
Universite Paris Descartes
University of Pennsylvania
Cliniques Universitaires Saint-Luc,…
University of Exeter
Oregon Health and Science University
KEY INSTITUTIONS0 1 2
Ajinomoto CO., INC.
Arkray, INC.
Korea Research Institute…
ViviaBiotech, S.L.
Bassa, Babu V.
Commisariat a l'Energie…
Glaser, Benjamin
Kowa CO., LTD.
Kyowa Hakko Kogyo…
KEY PATENTS
• Most prolific authors and institutions,
based on full-text searching for terms
and synonyms
• Patent assignee names from Reaxys

8. | 9
Research landscape analysis: collaboration
• Network of people and organizations collaborating in CHI space based on
co-authorship

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Finding mechanisms and targets: text mining
• Text mining of 25M abstracts, 3.5M Elsevier and non-Elsevier full texts
• normalization of concept names
• normalization of different ways of saying the same thing
• makes text compatible with other sources of information negative effect

10. | 11
Quick summary of what is known about CHI
• Text mining of 25M abstracts, 3.5M Elsevier and non-Elsevier full texts
• Identified proteins, small molecules, clinical parameters, diseases, and
biological functions, associated with CHI

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Building and refining the disease model
• Summary of the literature findings: CHI mutations
in the context of insulin secretion
• Generate hypotheses using:
 6.2M literature-extracted findings
 Functional annotations (e.g. Gene Ontology)
 >1800 pre-build pathways modeling disease and
normal states

12. | 13
From pathways to treatments:
PipelinePilot implementation combines data sources
Automated analysis combines bioassay data with pathway data
Find all targets that could
be used to affect the
disease state
Query for each target to find
the activities for each
compound that are >6 log units
Collate data by compound to summarize the
targets/activities related to disease that the
compound hits
• Compute geometric mean of activities for ranking
• Rank by number of targets and geometric mean of
activities against targets
Step 1 Step 2
Step 3

13. | 14
Automated analysis combines bioassay data with pathway data
From pathways to treatments:
• 88 Targets related to
hyperinsulinism with ≥3
literature references
• Full PathwayStudio
relationship information
• PathwayStudio also has all
compounds suggested as
treatments
Find all targets that could
be used to affect the
disease state
Step 1

14. | 15
Automated analysis combines bioassay data with pathway data
From pathways to treatments:
Find all targets that could
be used to affect the
disease state
Query for each target to find
compounds that have high
affinity for them (>6 log units)
Step 1 Step 2
Targets based on
text mining
Approved
compounds

15. | 16
Automated analysis combines bioassay data with pathway data
From pathways to treatments:
PipelinePilot implementation combines data sources
Mean of activities
among these targets
Mean of activities
among these targets
Targets and activities
for each compound
Drug-likeness
metrics for
sorting/classification
• All compounds that
were observed to bind
to targets in pathway
• Sorted by number of
active targets.
Too many targets may
suggest lack of specificity.
Find all targets that could
be used to affect the
disease state
Query for each target to find
compounds that have high
affinity for them (>6 log units)
Collate data by compound to summarize the
targets/activities related to disease that the
compound hits
• Compute geometric mean of activities for ranking
• Rank by number of targets and geometric mean of
activities against targets
Step 1 Step 2
Step 3

16. | 17
Approved compounds that may treat hyperinsulinism
• Each binds to one or
more targets related to
the disease
• Can easily be obtained
and tested in preclinical
studies
• List includes a
compound known to
treat hyperinsulinism,
sirolimus

17. | 18
From pathways to treatments:
PipelinePilot implementation output
Input:
“Congenital hyperinsulinism”
Output:
• Table of target information from PathwayStudio
• Table of compounds with targets, activities, and druglike parameters for each
compound
• SD file of compounds that may be efficacious, with clinical status (if any)
• Authors, Affiliations, Collaboration map
• List of papers

18. | 19
Power of combining pathway
data with experimentally
verified binding data
• Not just theoretical pathways -
testable hypotheses.
Results in testable
ideas
• Many compounds are
already approved drugs,
can be tested in in-vivo
experiments
Concepts can be extended
to find novel compounds
• Use modeling tools to extract
common frameworks
• SAR to optimize activity for
new indication
• Compare with compounds
suggested as treatments as
found by text mining
From pathways to treatments:
PipelinePilot implementation summary

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Example of 69 genetic modifiers of cystic fibrosis
1. Predict disease severity and
requirements for patient care
2. Find drugs ameliorating disease
symptoms and complications

20. | 21
Genetic modifiers for all Mendelian disorders
1. Prioritize genetic modifiers by the number of diseases they modify
2. Predict genetic modifiers for similar diseases
3. Predict genetic modifiers using pathway analysis

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• Figured out what is really needed
• Went through all the content, resources and tools we have in our
possession
• Which is possible because information is normalized
• Once the output of interest is decided: automated answer-generation
Provide disease name and get:
 Key Opinion Leaders and institutes
 List of targets with supporting information
 Sorted list of approved drugs with supporting information
Summary

22. | 23
Key message: Rare disease are no longer orphan
Drug repurposing for rare diseases
The cost of repositioning existing FDA approved drug for rare disease is now under
$500,000. This level of funding can be obtained through:
government grants
angel investment
patient organization funds
charities