This document discusses how SAP's in-memory database HANA could help address challenges in pharmaceutical research and development. It outlines data management challenges in pharma R&D, how HANA addresses these challenges through its real-time analytics capabilities, and potential use cases for HANA in areas like genomics, clinical trials, and drug development. The document proposes starting a discussion between pharma R&D and SAP/HPI to explore how HANA could help solve existing problems or future challenges.

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

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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 commercial
organizations from the global lifesciences industry 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 typical data management challenges found in big pharma R&D,
highlights the areas where HANA would be of biggest value, lists several R&D HANA use
cases, and proposes a number of ways how to start the conversation.

3. © 2013 SAP AG. All rights reserved. 3Confidential
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

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R&D innovations in life sciences
Where HANA could be used in pharma R&D
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
Assay development
Design/test/adapt assay
Transfer assay
In silico data acquisition
In silico design exper.
Target discovery
Genomics
Sequencing
Alignment
Variant calling
Annotation & analysis
Bioinformatics
Proteomics
Protein sequencing
Analysis1
HT screening
Primary screening
Secondary screening
Tertiary screening
Collect & analyze data
Lead development
Filter cluster compoun.
compoundsSynthesize compounds
Test compounds
Optimize leads
Filter cluster leads
Synthesize lead
Test compunds
Synthesize leads
Lead discovery
LT toxicity (2 species)
In vitro pharmacology
Synthesize compounds
Preclinical dev.
Translation. medicine
T1 Preclin. & P1 studies
T2 P2/P3 trials
T3 P4 & Outcomes Res.
T4 Population analysis
Tox check/safety
Pharmacodynamics
Pharmacokinetics
Animal testing
areas with potential use of HANA
1 For more information see www.proteomicsdb.org or https://www.youtube.com/v/ao4oStycKnw

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R&D innovations in life sciences
Proven benefits of HANA for genomics
Supported By: Carlos Bustamante lab
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
1,000x faster
tumor data analyzed in
seconds instead of hours
2-10 sec for
report execution

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R&D innovations in life sciences
Selected use cases for pharma R&D
Use cases for research Use cases for 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 non-
responders 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)

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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

8. Backup

9. © 2013 SAP AG. All rights reserved. 9Confidential
Backup: Analyst opinions
SAP is a leader in big data analytics
Forrester Wave: Big Data Predictive Analytics Solutions,
Q1/2013
Gartner Magic Quadrant for Data Warehouse Database Management
Systems, Feb 2013

10. © 2013 SAP AG. All rights reserved. 10Confidential
Any attribute
as index
Insert only
for time travel
Combined column
and row store
+
No aggregate
tables
Minimal
projections
Partitioning
Analytics on
historical data
t
Single and
multi-tenancy
SQL interface on
columns & rows
SQL
Reduction of
layers
x
x
Lightweight
Compression
Multi-core/
parallelization
On-the-fly
extensibility
+++
Active/passive
data storePA
+
+
++
T
Text Retrieval
and Extraction
Object to
relational mapping
Dynamic multi-
threading
within nodes
Map
reduce
No diskGroup Key
Bulk load
Backup: SAP HANA Features
In-Memory Building Blocks (1/2)

11. © 2013 SAP AG. All rights reserved. 11Confidential
Bulk load
Fast insertion of large
genomic datasets or
other relevant datasets
T
Text Retrieval
and Extraction
Text analytics engine for
both structured
/unstructured data,
integration with “R”
SQL interface on
columns & rows
Easily connect with
other tools (e.g.
Rstudio)
SQL
Lightweight
Compression
Fit big data in main
memory while allowing
fast retrieval
Multi-core/
parallelization
Speedup of relevant
queries across many
nodes
On-the-fly
extensibility
Adapting to new
format requirements
without going offline
(e.g. changing VCF files)
+++
Backup: SAP HANA Features
In-Memory Building Blocks (2/2)

12. Contact information:
Dr. Marc Maurer
Senior Global Account Executive
Email: marc.maurer@sap.com
Tel. +41 79 9642 42 90