SAP HANA Use Cases for Pharma Research & Development

Marc Maurer / September 9th 2013, v4

Why it could be beneficial for pharma R&Dto engage into a discussion about SAP HANA

© 2013 SAP AG. All rights reserved. 2Confidential

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.

1. Technology


Supports any Device

Any AppsAny App Server

SAP Business Suite and BW ABAP App Server

JSONR Open Connectivity

MDXSQL

Other AppsLocationsReal-timeHADOOPMachineUnstructuredTransaction

SAP HANA PlatformSQL, SQLScript, JavaScript

Replication, Streaming and ETL Integration Services

Search

Business Function Library

Data Virtualization

Text Mining

Predictive Analysis Library

DatabaseServices

Stored Procedure & Data Models

Planning Engine

Rules Engine

Application & UI Services

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.

SAP HANA Platform – More than just a databaseNext generation platform


SAP HANA Platform – More than just a databaseSAP HANA Innovations

Multi-core

architectureMassively parallel execution

High throughput sequencing

and analysis

Innovation Benefit Application

12 TB DRAM

servers in 2014 Large Data Sets in-memory Genomics, proteomics

and patient data

Compression (5-20x) Large data sets in-memoryGenomics, proteomics

and patient data

Combined Column

and Row Store Column = Fast Queries Adhoc queries using clinical data

Partitioning:

In-Database computingAnalyze large data sets

Complex computationsGenome alignment

Proteomics and Imaging data

+

No aggregate

tablesFlexible modeling

No data duplicationData Model for combined clinical

and omics data

Text Analytics Use of unstructured data Physician’s letters

Scientific LiteratureT


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

Bring data into SAP HANA for a high value scenario

Flexible and extremely fast analysis

Globalized

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

Pervasive Analytics

SAP HANA as a platform for all your analytics Multiple scenarios Consolidation database

that scales with multiple nodes

Unstructured data analysis (e.g. Text analytics)

Predictive analytics Analytics for mobile users

SAP HANA adoption modelA platform that scales



Multi-core



and analysis


12 TB DRAM


and patient data


and patient data

Combined Column


Partitioning:




+

No aggregate



and omics data



2. Proof points


SAP HANA and R&D: Proof pointsThe White House honors SAP in Nov 2013


SAP HANA and R&D: Proof pointsThe White House honors SAP in Nov 2013


SAP HANA and R&D: Proof pointsStrategic 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


SAP HANA and R&D: Proof pointsSAP 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



Multi-core



and analysis


12 TB DRAM


and patient data


and patient data

Combined Column


Partitioning:




+

No aggregate



and omics data



3. Use cases


R&D innovations in life sciencesChallenges 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


R&D innovations in life sciencesWhere HANA could be used in pharma R&D

Target identification

Define diseaseIdentify targetsCollect & analyze dataSelect targets

Target validation

Design validation exper.Validate drug targetsCollect & analyze dataSelect validate targets

Assay development

Design/test/adapt assayTransfer assayIn silico data acquisitionIn silico design exper.

Target discovery

Genomics

SequencingAlignmentVariant callingAnnotation & analysis

Bioinformatics

Proteomics

Protein sequencingAnalysis1

HT screening

Primary screeningSecondary screeningTertiary screeningCollect & analyze data

Lead development

Filter cluster compoun. compoundsSynthesize compoundsTest compounds

Optimize leads

Filter cluster leadsSynthesize leadTest compundsSynthesize leads

Lead discovery

LT toxicity (2 species) In vitro pharmacology

Synthesize compounds

Preclinical dev.

Translation. medicine

T1 Preclin. & P1 studiesT2 P2/P3 trialsT3 P4 & Outcomes Res.T4 Population analysis

Tox check/safety

PharmacodynamicsPharmacokineticsAnimal testing

areas with potential use of HANA

1 For more information see www.proteomicsdb.org or https://www.youtube.com/v/ao4oStycKnw

http://www.proteomics.org/

https://www.youtube.com/v/ao4oStycKnw

https://www.youtube.com/v/ao4oStycKnw


R&D innovations in life sciencesProven 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


R&D innovations in life sciencesSelected 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 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)

http://www.proteomics.org/

http://www.carecircles.com/



Multi-core



and analysis


12 TB DRAM


and patient data


and patient data

Combined Column


Partitioning:




+

No aggregate



and omics data



4. Next steps


R&D innovations in life sciencesHow 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

Contact information:

Dr. Marc MaurerSenior Global Account ExecutiveEmail: [email protected]. +41 79 9642 42 90

mailto:[email protected]