1. Shlomi Madar, Ph.D. | Signals | www.signalsgroup.com
Drug Repositioning and Repurposing:
Leverage Relevant & External Big Data
For Your Crucial Business Decisions

2. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
AGENDA
5:30 - 6:00 - Introduction & Drinks
6:00 - 6:10 - About Signals
6:10 - 7:00 - Drug Repositioning: Big Data to Big Opportunity
Efficient, Algorithm-Drive Methods for
Repeatable Repositioning Success
7:00 - 7:30 - Conclusion and Q&A Discussion
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3. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
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INTRODUCTION
TO SIGNALS

4. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE4
Meet Signals
Founded in 2009, 60+ employees and growing
Intelligence veterans, PhDs, data & computer scientists, industry experts
Intelligence platform & professional service
Serving over 30 Fortune 1000 clients
HQ in Israel; Offices in NYC and Geneva

5. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE5
Product Intelligence Experts
PRODUCT
INNOVATION
identify new product
opportunities and build a
business case
PRODUCT
RENOVATION
modify existing products to
capture market share
OPEN
INNOVATION
select competitive technology
strategies & partners
PRODUCT
LAUNCH
Develop go-to-market plans
and introduce new products
to market
PRODUCT
EXPANSION
grow market share via
introduction to new consumers
and geographies

6. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE6
Our Solution
AUTOMATED PRODUCTION AND DELIVERY OF BUSINESS ANALYTICS BASED ON OPEN WEB INTELLIGENCE
We collect, integrate and analyze big data from multiple sources, delivering concise findings at the decision point
CLOUD BASED APPLICATION DESIGNED FOR
DELIVERY OF ANALYTICS & INSIGHTS, SYNCED
WITH THE ORGANIZATIONAL NPD PROCESS
CORE PLATFORM FOR PRODUCING, MANAGING
AND STORING ANALYTIC MODELS, DYNAMIC
ONTOLOGIES AND COLLECTED BIG DATA

7. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Combine Multiple Source Types Into a Unified Decision Model
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8. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
THE CHALLENGE
Enhancing ROI on Drugs
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9. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
The Need
90% of Phase I drugs will fail to be approved by the FDA.
NATURE BIOTECHNOLOGY
Average cost for launching a successful drug: ~$2 Billion.
…and for a repositioned drug: ~$8.4 million
Drug Discovery World
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Patent Cliff. Drugs’ patents are expiring. In 2015,
products worth $66 billion will lose IP protection2.
Fierce Pharma

10. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
The Need
90% of Phase I drugs will fail to be approved by the FDA.
NATURE BIOTECHNOLOGY
Average cost for launching a successful drug: ~$2 Billion.
…and for a repositioned drug: ~$8.4 million
Drug Discovery World
10
Patent Cliff. Drugs’ patents are expiring. In 2015,
products worth $66 billion will lose IP protection2.
Fierce Pharma
ROI on Any drug, either
approved or under
development –
can be enhanced

11. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Drug Repositioning – Success Stories
1Thomson Reuters
Drugs that have been successfully repositioned1
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12. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Innovative Ideas Supported by New Technologies
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13. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Innovative Ideas Supported by New Technologies
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14. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
NEW APPROACH
FOR DRUG
REPOSITIONING
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15. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Detection of Novel Opportunities For Drug-x
Identifying similar drugs
that already benefit from
combination therapy and
are potentially relevant for
Drug X.
Modifications of
dosage, route of
administration, etc.
Research for conditions
associated with a
personalized medicine
approach in relevant
research literature.
For example, an ‘Omics’-
related finding associated
with a certain disease that
can later be targeted by
specific drugs.
Modifications Conditions Combinations Genomics
Drug X
Four approaches can be
taken in order to detect novel
opportunities for Drug X.
Similar
Drugs
Predicted
Adverse Drug
Reactions
A gauge is utilized for
assessment of similarity
level to Drug X.
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16. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Methodology
Breakdown of Drug X traits into several levels (pathway, mechanism of action,
etc.) based on data mining and natural language processing.
Queries are generated and applied onto the different resources.
Applying queries to extract similar therapeutic entities and assess their similarity
to Drug X.
Similar Therapeutic Entities
Drug X Characterization
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Top Hits
Identifying Novel Conditions, Combinations, Modifications, and Genomic Data -
new opportunities for increasing ROI from drug X
Conditions Ranking Model
Establishing a ranking model which will reflect the market conditions and the
client’s preferences and core capabilities. The end result will be a list of the most
relevant conditions that could be potentially treated with the initial drug.

17. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Evidence-Based Data Model
Converting Multiple Data Sources Into Meaningful Insights
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DRUG DATA VOCABULARIES SCIENTIFIC RESOURCES
• DrugBank
• Drugs.com
• FDA NDC
• FDA Pharmacological Classes
• FDA Product Labels
• FDA UNII
• RX Norm
• WebMD.com
• MeSH Descriptors
• MeSH Tree
• WHO ATC
• FDA Structural Classes
• FDA Mechanism of Action
• FDA Physiological Effect
• ClinicalTrials.gov
• PubMed.gov
PATENTS
• Espacenet.org
GENOMICS
• GEO
• KEGG
• Pharmgkb
• FINDbase
• GMOD

18. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Input: “Drug X” Natural
Language
Processing
Query
Generation
Keywords for
Drug X
Characterization
• The input into Signals’ Drugbase includes Drug X with all of its synonyms.
Search
Drug X
Converting Multiple Data Sources Into Meaningful Insights
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19. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Query
Generation
Keywords for
Drug X
Characterization
Input: “Drug X” Natural
Language
Processing
• The list of terms undergoes a manual
curation. The process produced >95%
accuracy.
Converting Multiple Data Sources Into Meaningful Insights
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• The initial input undergoes natural language processing to generate an output of terms which characterize
Drug X.

20. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Input: “Drug X”
Natural
Language
Processing
Query
Generation
Keywords for
Drug X
Characterization
• The initial input undergoes natural
language processing to generate
an output of terms which
characterize Drug X.
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Converting Multiple Data Sources Into Meaningful Insights

21. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Hierarchy Tree of Search Terms Based on Mode of Action Similarities to Drug X
Pro-Inflammatory/
Anti-Inflammatory
Cell Type
C
Cytokines
Immune
Cells
Protein
X
Cell B
Path D
Path CPath B
Cell Type B
Path A
Cell Type
A
Cell A
Cell Type
C Markers
Path D
CDXX
CDxx
CDxxCDxxThXXThXThXThX
Path HPath GIL-XXIL-X Path IIL-XXIL-XXIL-XIL-X
Down-regulation
Up-regulation
Tier 2 affected by Drug X
Tier 1 affected by Drug X
Neuroprotection
Cell Type
E
Cell Type
G
Cell Type
F
Cell Type
H
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22. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Queries are generated to include all terms, their synonyms, and the type of modifications
undergone by Drug-X, which are then applied onto the different resources.
Input: “Drug-
X”
Natural
Language
Processing
Query
Generation
Keywords for
Drug-X
Characterization
Converting Multiple Data Sources Into Meaningful Insights
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23. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
IL-48 Path 1 Th-X TNFα NF-κB
Downregulation Downregulation AND IL-48 Downregulation AND Path 1 Downregulation AND Th-X Downregulation AND TNFα Downregulation AND NF-κB
Prevent Prevent AND IL-48 Prevent AND Path 1 Prevent AND Th-X Prevent AND TNFα Prevent AND NF-κB
Barrier Barrier AND IL-48 Barrier AND Path 1 Barrier AND Th-X Barrier AND TNFα Barrier AND NF-κB
Block Block AND IL-48 Block AND Path 1 Block AND Th-X Block AND TNFα Block AND NF-κB
Decrease Decrease AND IL-48 Decrease AND Path 1 Decrease AND Th-X Decrease AND TNFα Decrease AND NF-κB
Reduce Reduce AND IL-48 Reduce AND Path 1 Reduce AND Th-X Reduce AND TNFα Reduce AND NF-κB
Diminish Diminish AND IL-48 Diminish AND Path 1 Diminish AND Th-X Diminish AND TNFα Diminish AND NF-κB
Eliminate Eliminate AND IL-48 Eliminate AND Path 1 Eliminate AND Th-X Eliminate AND TNFα Eliminate AND NF-κB
Anti Anti AND IL-48 Anti AND Path 1 Anti AND Th-X Anti AND TNFα Anti AND NF-κB
Inhibit Inhibit AND IL-48 Inhibit AND Path 1 Inhibit AND Th-X Inhibit AND TNFα Inhibit AND NF-κB
Obstruct Obstruct AND IL-48 Obstruct AND Path 1 Obstruct AND Th-X Obstruct AND TNFα Obstruct AND NF-κB
Against Against AND IL-48 Against AND Path 1 Against AND Th-X Against AND TNFα Against AND NF-κB
Antagonist Antagonist AND IL-48 Antagonist AND Path 1 Antagonist AND Th-X Antagonist AND TNFα Antagonist AND NF-κB
De-activate Deactivate AND IL-48 Deactivate AND Path 1 Deactivate AND Th-X Deactivate AND TNFα Deactivate AND NF-κB
De-activate De-activate AND IL-48 De-activate AND Path 1 De-activate AND Th-X De-activate AND TNFα De-activate AND NF-κB
Dysregulate Dysregulate AND IL-48 Dysregulate AND Path 1 Dysregulate AND Th-X Dysregulate AND TNFα Dysregulate AND NF-κB
Suppress Suppress AND IL-48 Suppress AND Path 1 Suppress AND Th-X Suppress AND TNFα Suppress AND NF-κB
Upregulation Upregulation AND IL-48 Upregulation AND Path 1 Upregulation AND Th-X Upregulation AND TNFα Upregulation AND NF-κB
Promote Promote AND IL-48 Promote AND Path 1 Promote AND Th-X Promote AND TNFα Promote AND NF-κB
Enhance Enhance AND IL-48 Enhance AND Path 1 Enhance AND Th-X Enhance AND TNFα Enhance AND NF-κB
Increase Increase AND IL-48 Increase AND Path 1 Increase AND Th-X Increase AND TNFα Increase AND NF-κB
Augment Augment AND IL-48 Augment AND Path 1 Augment AND Th-X Augment AND TNFα Augment AND NF-κB
Induce Induce AND IL-48 Induce AND Path 1 Induce AND Th-X Induce AND TNFα Induce AND NF-κB
Agonist Agonist AND IL-48 Agonist AND Path 1 Agonist AND Th-X Agonist AND TNFα Agonist AND NF-κB
Activate Activate AND IL-48 Activate AND Path 1 Activate AND Th-X Activate AND TNFα Activate AND NF-κB
Amplify Amplify AND IL-48 Amplify AND Path 1 Amplify AND Th-X Amplify AND TNFα Amplify AND NF-κB
Amplification Amplification AND IL-48 Amplification AND Path 1 Amplification AND Th-X Amplification AND TNFα Amplification AND NF-κB
An elaborate matrix is generated in order to produce queries with the utmost accuracy and coverage.
Breadth and Depth of Search Queries
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24. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
THE OUTCOME
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25. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Drug Database
Clinical Trial
Publication
Source
Drug X's Most Similar Drugs – Shared Molecular Pathways
Drug A
Drug B
Vitamin A
Drug C
Drug D
Drug E
Drug F
Drug G
Path-1 Path-2 Path-3 Path-4 Path-5 Path-6 Path-7 Path-8 Path-9 Gene1 Gene2 Gene3 Gene4 Gene5 Gene6 Gene7 Gene8 Gene9
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26. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
No. of Data Points
Novel Conditions by Similar Drugs
Numbers represent the data collected from patents, publications, labels and clinical trials.
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Condition A
Condition B
Condition C
Condition D
Condition E
Condition F
Condition G
Condition H
Condition I
Condition J
Condition K
Condition L
Condition M
Condition N
Condition O
Condition P
Condition Q
Condition R
Drug A
Drug B
Vitamin A
Drug C
Drug D
Drug E
Drug F
Drug G
Drug H
Similar Drugs

27. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Predicted Adverse Events Stemming From Similar Drugs
Injection
Oral
Topical
Drug A Drug B Vitamin A Drug C Drug D Drug E Drug F Drug G Drug XDrug H
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28. NEW APPROACH FOR DRUG REPOSITIONING
PRODUCT INTELLIGENCE
Signals’ Recommendations for Drug X
Using Drug X in combination with Vitamin A1
2
3
4
Treating Rheumatoid Arthritis with Drug X
Abdominal Pain, weight decrease, and a cough are predicted adverse events
for Drug X
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A-123 gene polymorphisms are potential pharmacogenetic markers for Drug X
The client should follow up on their patents combining Drug X with Drug Y
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29. See More of Our Work Here | www.signalsgroup.com | info@signalsgroup.com
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