This document discusses how SAP's in-memory database platform called SAP HANA could benefit pharmaceutical research and development. It provides an overview of SAP HANA's technology capabilities such as massively parallel processing, large in-memory datasets, and text analytics. It then discusses proof points of SAP HANA's use in genomics to dramatically speed up DNA analysis. The document outlines potential use cases of SAP HANA in areas like secondary analysis of genome data, clinical trial data analysis, and virtual patient simulation. It concludes by recommending next steps for pharmaceutical companies to start

1. Why it could be beneficial for pharma R&D
to engage into a discussion about SAP HANA
Marc Maurer / September 9th 2013, v4

2. Intention of this slide deck
 In the past 40 years, SAP has been known as the world’s leader for ERP applications.
 Over the last few years, SAP did undergo a major transformation to dramatically
broaden its portfolio and to come up with a breakthrough technology named SAP HANA.
 This technology represents an in-memory based real-time data/analytics platform that
is especially suited to adress the data management challenges of big pharma R&D.
 The Hasso Plattner Institute (HPI), SAP, and a number of academic and big pharma
firms are currently collaborating to plan and implement a number of different HANA use
cases.
 We believe that it would be beneficial for pharma R&D to start a discussion with
SAP/HPI to learn about use cases and to explore how to adress existing problems or
future challenges.
 This slide deck adresses on a high-level the technology, some proof points, pharma R&D
use cases, and a number of ways how to continue the conversation.
© 2013 SAP AG. All rights reserved.
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3. 1. Technology

4. SAP HANA Platform – More than just a database
Next generation platform
Any Apps
SAP Business Suite
Any App Server
Supports
any Device
and BW ABAP App Server
SQL
MDX
R
JSON
Open
Connectivity
SAP HANA Platform
SQL, SQLScript, JavaScript
Search
Data
Virtualization
Text Mining
Stored Procedure
& Data Models
Application &
UI Services
Business
Function Library
Predictive
Analysis Library
Database
Services
Planning
Engine
Rules Engine
Replication, Streaming and ETL Integration Services
Transaction
Unstructured
Machine
HADOOP
Real-time
Locations
Other Apps
SAP HANA Platform Converges Database, Data Processing and Application Platform Capabilities &
Provides Libraries for Predictive, Planning, Text, Spatial, and Business Analytics to enable business to
operate in real-time.
© 2013 SAP AG. All rights reserved.
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5. SAP HANA Platform – More than just a database
SAP HANA Innovations
Innovation
Massively parallel execution
High throughput sequencing
and analysis
12 TB DRAM
servers in 2014
Large Data Sets in-memory
Genomics, proteomics
and patient data
Compression (5-20x)
Large data sets in-memory
Genomics, proteomics
and patient data
Combined Column
and Row Store
Column = Fast Queries
Adhoc queries using clinical data
Partitioning:
In-Database computing
Analyze large data sets
Complex computations
Genome alignment
Proteomics and Imaging data
No aggregate
tables
T
Application
Multi-core
architecture
+
Benefit
Flexible modeling
No data duplication
Data Model for combined clinical
and omics data
Text Analytics
Use of unstructured data
Physician’s letters
Scientific Literature
© 2013 SAP AG. All rights reserved.
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6. SAP HANA adoption model
A platform that scales
Globalized
Pervasive Analytics
SAP HANA as a platform
for all your analytics
Localized
SAP HANA as a data mart
 Public available proteomics
database with 50 TB storage
and 160 processing units
 11,000 datasets from human
cancer cell lines, tissues and
body fluids
 Covers 92% of the Human
proteome
© 2013 SAP AG. All rights reserved.
 Multiple scenarios
 Consolidation database
that scales with multiple
nodes
 Unstructured data analysis
(e.g. Text analytics)
 Predictive analytics
 Analytics for mobile users
SAP HANA as a platform
for analytics and
applications
 Genomic DNA analysis in
real-time to transform
cancer patient care
 Increased speed, accuracy
and visibility for drug
discovery
 Real-time Big data
(R+Hadoop+HANA)
 408,000 faster than
traditional disk-based
system
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7. SAP HANA Platform – More than just a database
SAP HANA Innovations
2. Proof points
Innovation
Massively parallel execution
High throughput sequencing
and analysis
12 TB DRAM
servers in 2014
Large Data Sets in-memory
Genomics, proteomics
and patient data
Compression (5-20x)
Large data sets in-memory
Genomics, proteomics
and patient data
Combined Column
and Row Store
Column = Fast Queries
Adhoc queries using clinical data
Partitioning:
In-Database computing
Analyze large data sets
Complex computations
Genome alignment
Proteomics and Imaging data
No aggregate
tables
T
Application
Multi-core
architecture
+
Benefit
Flexible modeling
No data duplication
Data Model for combined clinical
and omics data
Text Analytics
Use of unstructured data
Physician’s letters
Scientific Literature
© 2013 SAP AG. All rights reserved.
Confidential
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8. SAP HANA and R&D: Proof points
The White House honors SAP in Nov 2013
© 2013 SAP AG. All rights reserved.
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9. SAP HANA and R&D: Proof points
The White House honors SAP in Nov 2013
© 2013 SAP AG. All rights reserved.
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10. SAP HANA and R&D: Proof points
Strategic partnership between SAS and SAP
Overview:
Deliver a joint technology, product and GTM roadmap that will leverage SAP HANA
in-memory platform and SAS advanced analytics. Bring 5 SAS industry applications
on SAP HANA and validate with pilot customers while delivering on the strategic
roadmap by 1H 2014.
Proposal:
A phased approached where SAP and SAS can immediately deliver immense
customer value with the following:

Embedding SAS predictive model scoring and selected algorithms for direct use in SAP
HANA to reduce the “data to compute” distance
 Deliver 5 industry SAS solutions on SAP HANA and “powered by HANA”
 Mid to long term, bring additional SAS algorithms to SAP HANA, optimize selected SAS
solutions for SAP HANA and deliver on a larger GTM for an expanded set of customers
© 2013 SAP AG. All rights reserved.
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11. SAP HANA and R&D: Proof points
SAP is a leader in big data analytics
Gartner Magic Quadrant for Data Warehouse Database Management
Systems, Feb 2013
© 2013 SAP AG. All rights reserved.
Forrester Wave: Big Data Predictive Analytics
Solutions, Q1/2013
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12. SAP HANA Platform – More than just a database
SAP HANA Innovations
3. Use cases
Innovation
Massively parallel execution
High throughput sequencing
and analysis
12 TB DRAM
servers in 2014
Large Data Sets in-memory
Genomics, proteomics
and patient data
Compression (5-20x)
Large data sets in-memory
Genomics, proteomics
and patient data
Combined Column
and Row Store
Column = Fast Queries
Adhoc queries using clinical data
Partitioning:
In-Database computing
Analyze large data sets
Complex computations
Genome alignment
Proteomics and Imaging data
No aggregate
tables
T
Application
Multi-core
architecture
+
Benefit
Flexible modeling
No data duplication
Data Model for combined clinical
and omics data
Text Analytics
Use of unstructured data
Physician’s letters
Scientific Literature
© 2013 SAP AG. All rights reserved.
Confidential
12

13. R&D innovations in life sciences
Challenges in pharma R&D and how HANA adresses them
Challenges of data analysis and data
management in big pharma
Characteristics of HANA
Thight integration of scientific data and analysis algorithms
as relevant scientific data is usually distributed over many
locations and stored in many different formats
 User can implement domain-specific application logic
(from high level SQLscript, full support of all "R" libraries
to native function libraries)
 All application logic is executed directly on data; no need
of data transfer between different systems
As the different activities for development (e.g. assays,
disease models, etc.) need to be transparent, versioning of
algorithms and data is important
 Every calculation model (algorithm) in HANA is registered
in a repository; easy to re-create previous analysis steps
 Every data record is associated with a transaction
identifier; records can be mapped to revisions of
calculation models to allow versioning
Support non-relational data structures and operations
 HANA supports data structures such as graphs to avoid
emulating them on top of relational data (which often
results in poor performance)
Support of big data initiatives
 HANA is integrated with map reduce implementations
such as Hadoop to allow parallel exploitation of big data
sources
Intuitive interface to design analysis pipelines, a system that
is accessible to a wide range of users with a broad range of
skill sets (scientists, analysts, developers)
 Analysis pipelines are defined via a graphical user
interface in HANA Studio
 Researchers can compare results generated by different
pipelines
© 2013 SAP AG. All rights reserved.
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14. R&D innovations in life sciences
Where HANA could be used in pharma R&D
Target discovery
Bioinformatics
Lead discovery
Preclinical dev.
Genomics
HT screening
Translation. medicine
Target identification
Define disease
Identify targets
Collect & analyze data
Select targets
Target validation
Design validation exper.
Validate drug targets
Collect & analyze data
Select validate targets
Sequencing
Alignment
Variant calling
Annotation & analysis
Proteomics
Protein sequencing
Analysis1
Assay development
Design/test/adapt assay
Transfer assay
In silico data acquisition
In silico design exper.
Primary screening
Secondary screening
Tertiary screening
Collect & analyze data
Lead development
Filter cluster compoun.
compounds compounds
Synthesize
Test compounds
Synthesize compounds
T1
T2
T3
T4
Preclin. & P1 studies
P2/P3 trials
P4 & Outcomes Res.
Population analysis
Tox check/safety
Pharmacodynamics
Pharmacokinetics
Animal testing
Optimize leads
Filter cluster leads
Synthesize lead
Test compunds
Synthesize leads
LT toxicity (2 species)
In vitro pharmacology
areas with potential use of HANA
1 For more information see www.proteomicsdb.org or https://www.youtube.com/v/ao4oStycKnw
© 2013 SAP AG. All rights reserved.
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15. R&D innovations in life sciences
Proven benefits of HANA for genomics
408,000x faster than
traditional disk-based
systems in technical
Proof of Concept
216x faster DNA
analysis result – from
2-3 days to 20 minutes
Supported By: Carlos Bustamante lab
1,000x faster
tumor data analyzed in
seconds instead of hours
2-10 sec for
report execution
© 2013 SAP AG. All rights reserved.
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16. R&D innovations in life sciences
Selected use cases for pharma R&D
Use cases for pharma research
Use cases for pharma development
 Secondary and tertiary analysis of genome data:
Reduce time to analyse genome processing pipelines to
minutes and hours. Automatic search in structured and
unstructured data sources including entity extraction. For
proteomics there is also a public available proteomics
database powered by HANA (see www.proteomicsdb.org)
 Clinical trial data cleansing: Automatic reformatting of
clinical trial data from one format to another, automatic
systematic quality monitoring to save outsourcing costs
and clinical trial throughput speed.
 Speeding up pathway analysis: Executing complex
queries like «find a new molecule able to dock to kinase
XYZ to inhibit enzymatic activity» much faster.
 Clinical trial design: Analysis of patient cohorts in
realtime; to make trial protocol adaptations ad hoc and
saving time during trial design phase.
 3D structures: Representing genomic/proteine structures
in 3D e.g. to visually explore genetic pathways or
comparing gene sections with a genome reference
database (to identfy variants/mutations).
 Patient recruiting optimization: Iincreasing forecast
accuracy for recruiting patients into trials and addressing
questions like how to select the right investigator, etc.
 Virtual patient simulation: Combining molecular patient
data with models of tumor cells to simulate the effects of
different drugs.
 Clinical trial optimization: Data platform to increase
performance for trial simulations and integrating internal
and external data sources.
 Interorganizational data analysis: Several HANA
instances in different research/healthcare organizations
allow cross-analysis without moving confidential data
between the organizations.
 Fallen angels: Re-analysis of failed clinical trials where
HANA could identify variants that responders and nonresponders have in common to propose companion
diagnostic in order to recover investments into failed
trials.
Other use cases: Trial fraud management, risk-based trial monitoring,
iRise clinical trial app, patient engagement apps (www.carecircles.com)
© 2013 SAP AG. All rights reserved.
Confidential
16

17. SAP HANA Platform – More than just a database
SAP HANA Innovations
4. Next steps
Innovation
Massively parallel execution
High throughput sequencing
and analysis
12 TB DRAM
servers in 2014
Large Data Sets in-memory
Genomics, proteomics
and patient data
Compression (5-20x)
Large data sets in-memory
Genomics, proteomics
and patient data
Combined Column
and Row Store
Column = Fast Queries
Adhoc queries using clinical data
Partitioning:
In-Database computing
Analyze large data sets
Complex computations
Genome alignment
Proteomics and Imaging data
No aggregate
tables
T
Application
Multi-core
architecture
+
Benefit
Flexible modeling
No data duplication
Data Model for combined clinical
and omics data
Text Analytics
Use of unstructured data
Physician’s letters
Scientific Literature
© 2013 SAP AG. All rights reserved.
Confidential
17

18. R&D innovations in life sciences
How to start the conversation
 Webconference with specialists from HPI/SAP to discuss other use cases
available, answer questions, and find possibilities for on-site interactions
 On-site workshop with one of the following three scenarios:
 Focused approach based on concrete customer ideas and requirements
 Use case approach leveraging experience of other intiatives with other partners
 1-day design thinking workshop to discover new and radically different ways for solving
a data-related research problem of customer
 M310 course: 6 students from Stanford university work two days a week for 9 months on
a specific customer problem including documentation and prototype
© 2013 SAP AG. All rights reserved.
Confidential
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19. Contact information:
Dr. Marc Maurer
Senior Global Account Executive
Email: marc.maurer@sap.com
Tel. +41 79 9642 42 90