mobilizing informational resources for rare diseases

Maria Shkrob, PhD, Project Manager, Elsevier Professional Servicesm.shkrob@elsevier.com

March 8, 2016

Mobilizing informational resources for rare diseasesWhen every piece matters

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• 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.

is partnering with Findacure scientists to help identify and evaluate treatments for this devastating disease.

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Congenital hyperinsulinism libraryIn support of Findacure’s mission of education and knowledge sharing:

• Collection of papers focused on CHI sorted by disease and study type• Access to all Elsevier’s ScienceDirect full-text publications covering CHI

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Why do we need literature?

PLACESPEOPLE

GENES

DRUGS

INTERACTIONSPROPERTIES

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The power of processed contentPEOPLE

GENES

DRUGS

INTERACTIONSPROPERTIES

DATA NORMALIZATION DATABASES TOOLS

PLACES

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Research landscape analysis: connecting patients, researchers and institutions

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.

0 10 20 30 40 50 60 70KEY AUTHORS

The Children's Hospital of PhiladelphiaUCL Institute of Child Health

Hopital Necker Enfants MaladesUniversity of Pennsylvania, School of Medicine

UCLUniversite Paris DescartesUniversity of Pennsylvania

Cliniques Universitaires Saint-Luc, BrusselsUniversity of Exeter

Oregon Health and Science University

0 10 20 30 40 50 60 70 80KEY INSTITUTIONS

Ajinomoto CO., INC.

Arkray, INC.

Korea Research Institute of Chemical Technology

ViviaBiotech, S.L.

Bassa, Babu V.

Commisariat a l'Energie Atomique

Glaser, Benjamin

Kowa CO., LTD.

Kyowa Hakko Kogyo CO., LTD

0 1 2KEY 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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High level summary of full text publicationsTag cloud of titles and sentences discussing hyperinsulinism:• Provides a very high level summary of a group of publications• Gives overview of the terms and words being used when discussing the

disease

Sized by inversed document frequency (IDF), colored by term frequency (TDF)

Sized by relevance, colored by trend

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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

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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

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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

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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

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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

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Automated analysis combines bioassay data with pathway data

From pathways to treatments:

Mean of activities among these targets

Mean of activities among these targetsTargets 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

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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

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From pathways to treatments:PipelinePilot implementation outputInput:“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

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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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Findacure: empowering patient groups and facilitating treatment development

Parents:• Learn more about the disease• Find doctors and medical centers

Doctors:• Learn more about the disease• Explore case studies• Collaborate

Researchers:• Testable ideas for repurposing of generic drugs• Knowledgebase to support the research of the disease

mechanisms• Collaborate

Evidence to support 10 drug repurposing trials

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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-generationProvide disease name and get:

KOLs and institutes List of targets with supporting information Sorted list of approved drugs with supporting information

Summary

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Findacure / Elsevier collaboration

Dr Rick ThompsonFindacure

Dr Nicolas SireauFindacure

Dr Matthew ClarkElsevier

Dr Maria ShkrobElsevier

Thank you

Welcome to our booth

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