Anton Yuryev, PhD, Elsevier Professional Services

a.yuryev@elsevier.com

November 1, 2017

Mobilizing informational resources for rare diseasesWhen every piece matters

| 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 nutraceuticalscan 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 orphanDrug repurposing for rare diseases

| 3

• A rare genetic disease

• Permanently excessive level of

insulin in the blood

• Develops within the first few days of lifeSymptoms 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

Congenital hyperinsulinism libraryEducation 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

Why do we need literature?

PLACES

PEOPLE

GENES

DRUGS

INTERACTIONSPROPERTIES

| 6

The power of processed content

PEOPLE

GENES

DRUGS

INTERACTIONSPROPERTIES

DATA NORMALIZATION DATABASES TOOLS

PLACES

| 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

| 9

Research landscape analysis: collaboration

• Network of people and organizations collaborating in CHI space based on

co-authorship

| 10

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

| 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

| 12

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

| 13

From pathways to treatments:

PipelinePilot implementation combines data sourcesAutomated 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 2Step 3

| 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

| 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

| 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 2Step 3

| 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

| 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

| 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

| 20

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

| 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

| 22

• 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

| 23

Key message: Rare disease are no longer orphanDrug 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