Jabe Wilson, Director, Text and Data Analytics, Elsevier

jabe.wilson@elsevier.com

28 February, 2017

Mobilizing informational resources for rare diseasesWhen every piece matters

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Rare diseases – when every piece matters

Nick Sireau at TEDx ImperialCollege

https://www.youtube.com/watch?v=B4UnVlU5hAY

• No support

• No funding

• No treatments

is a UK charity that is building the rare disease community to raise awareness,

drive research and develop treatments.

is partnering with Findacure scientists to help identify and evaluate treatments

for congenital hypersinsulinism

• Patients community

• Collaboration with medical

researchers

• Drug repurposing candidate

• Fundraising

• Clinical Trial

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Findacure / Elsevier collaboration

Dr Rick Thompson

Findacure

Dr Nicolas Sireau

Findacure

Dr Matthew Clark

Elsevier

Dr Maria Shkrob

Elsevier

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Why do we need literature?

PLACES PEOPLE GENES

DRUGS INTERACTIONSPROPERTIES

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Why text mining?

Amorphous information Structured information

Image Source: http://www.thesocialleader.com/wp-content/uploads/2011/03/paper-piles.jpg

Text mining: analyzing text to extract information that is useful for particular purposes

Text

mining

• Hard to deal with

• Hard to deal with algorithmically

• Not scalable

• Search

• Visualize

• Network analysis

• Scalable

• Compressed

20km

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• CHI Library

• Disease, Target, Pathway, andCompound Analysis

• Research Landscape Analysis

Information Assets Applied

• Content

Elsevier’s vast set of literature and patent data

• Data normalization

Taxonomies and dictionaries to normalizeauthor names, institutions, drugs, targets, andother important terms

• Information extraction

Finding semantic relationships, targets,pathways, drugs, and bioactivities

Creating a comprehensive view of CHI with Elsevier

R&D Solutions

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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

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Research landscape analysis: collaboration

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

co-authorship

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• CHI in abstract or title

• CHI subtypes

• By publication type

• By study type

(including MeSH terms)

CHI: finding relevant documents

Indicate what to query

Filter by study type

Specify distanceFinding documents that mention certain

aspects of CHI

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CHI: finding targets, drugs, and drug effects

"protein"

"terms for

genetic

variations"

"Persistent

Hyperinsulinemia

Hypoglycemia of Infancy"

Relevant Text Title AuthorsReference

DateDOI

ABCC8 mutation Persistent

Hyperinsulinemia

Hypoglycemia of Infancy

In the literature, nine genes have been reported to

be associated with CHI , with the most common

genetic causes of CHI being mutations in either

ABCC8 or KCNJ11 .

Successful treatment of a newborn

with congenital hyperinsulinism having

a novel heterozygous mutation in the

ABCC8 gene using subtotal

pancreatectomy

Yen C.-F, Huang C.-Y,

Chan C.-I, Hsu C.-H, Wang

N.-L, Wang T.-Y, Lin C.-L,

Ting W.-H.

2016 10.1016/j.

tcmj.2016

.04.001

ABCC8 loss of function

mutation

Persistent

Hyperinsulinemia

Hypoglycemia of Infancy

GOF and loss-of function mutations in KCNJ11

(Kir6.2) and ABCC8 (SUR1), which encode the

predominant KATP channel subunits in

pancreatic β-cells and in neurons, are now well-

understood to underlie neonatal diabetes and

congenital hyperinsulinism, respectively.

Adenosine Triphosphate-Sensitive

Potassium Currents in Heart Disease

and Cardioprotection

Nichols C.G. 2016 10.1016/j.

ccep.201

6.01.005

ATP-activated inward

rectifier potassium

channel

mutation Persistent

Hyperinsulinemia

Hypoglycemia of Infancy

The prevalence of KATP channel gene mutations,

diazoxide responsiveness, and rates for surgery

is broadly commensurate with other CHI cohorts.

Feeding Problems Are Persistent in

Children with Severe Congenital

Hyperinsulinism

Banerjee I, Forsythe L,

Skae M, Avatapalle HB,

Rigby L, Bowden LE,

Craigie R, Padidela R,

Ehtisham S, Patel L,

Cosgrove KE, Dunne MJ,

Clayton PE.

2016 10.3389/f

endo.201

6.00008

Extracting structured information from text

Standardized

names

Standardized

link

Evidence

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CHI: summarization and visualization of the findings

• Visualization and summarization of

6.2 M literature findings

• Linking to non-literature sources

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Automated analysis combines bioassay data with text-mined data

From pathways to treatments:

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 protein to find

compounds that target it (>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

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• Used extensive Elsevier’s content, tools and capabilities to provide

information about a rare disease:

Text Mining to find targets and summarize what is known about the

disease mechanism

Bioactivity data to find drugs that target those targets

Normalized names of authors and institution to find collaborators

• Once the output of interest is decided, answer generation can be

automated:

Provide disease name and get:

List of targets with supporting information

Sorted list of approved drugs with supporting information

KOLs and institutes

Summary

Thank you

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