Next Generation Information Management Systems for Research, Development and Decision Support

Next Generation Information Management Systems for Research, Development and Decision Support Dr. Werner Eberhardt, SAP AG Paris July 11, 2013 Matthias Steinbrecher, ICP, Berlin Dr. Matthieu-P. Schapranow, HPI, Berlin Barbara Stortz, SAP, Palo Alto Enakshi Singh, SAP, Palo Alto Emanuel Ziegler, SAP, Walldorf Dr. Judith Schlegl, SAP, Walldorf Dr. Stefan Scheidl, SAP, Walldorf

What if we create a bridge between the digital and the medical revolution Comparison of Costs for Main Memory and Genome Analysis 10000 1000 Costs per Megabyte RAM Costs per Megabase Sequencing Costs in USD 100 10 1 0,1 0,01 0,001 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 SAP AG. All rights reserved. 4

Why now? Next Generation Information Management Drug R&D Costs Exploding One Size no longer fits All Overload of Information In Memory Mobile, Cloud, Collaboration 16x increase of expenditure for a new drug between 1995 and 2010 Which therapy (combination) to pick with 40+ drugs on the market and 60+ in phase III clinical trials while there are often multiple driver mutations involved Physicians and scientists are increasingly unable to stay on top of scientific progress in personalized cancer therapy Emergence of enabling IT Technology HITECH 2009: $36B invest for meaningful use of EHRs 2013 SAP AG. All rights reserved. 6

Why now Oncology to become a Data driven Science causal mutations are different from patient to patient, and evolve over time 1 million human genomes fully sequenced by end of 2013, 5 million by 2014 About 400 protein-coding genes showed somatic mutations driving tumor growth 15 million new cancer patients each year As many as 20 driver mutations possible for a single cancer cell 9 days for the analysis of a patient s sequencing data Embrace Complexity B I G D A T A 2013 SAP AG. All rights reserved. 7

Why now Our Contribution SAP HANA is a revolutionary in-memory platform that helps you process massive amounts of data, and deliver information at unprecedented speeds, up to 10,000 times faster. Applications Analytics Mobile Database and Technology Cloud SAP Patient Management Siemens i.s.h.med Healthcare Analytics Foundation Mobile EMR Sybase Mobile EMR Sybase Successfactors Enterprise Cloud A real-time business platform, powered by SAP HANA 2013 SAP AG. All rights reserved. 8

Our Contribution SAP HANA Innovations Innovation Benefit Application Multi-core architecture 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 Flexible modeling No data duplication Data Model for combined clinical and omics data T Text Analytics Use of unstructured data Physician s letters Scientific Literature 2013 SAP AG. All rights reserved. 10

Our Contribution Examples MITSUI KNOWLEDGE Bio-Informatik 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 CHARITÉ Klinikum 1,000x faster tumor data analyzed in seconds instead of hours 2-10 sec for report execution 2013 SAP AG. All rights reserved. 11

Solution Components Genomics and Proteomics

Solution Components Health Platform as a Data Warehouse and Basis for Collaboration

Solution Components Health Platform as a Warehouse Information and Feedback within the Window of Opportunity Patients Physicians Insurers Research Real-Time Data Capture and Analysis SAP HANA Healthcare Platform Genomics Electronic Medical Records Annotations... All relevant Medical Information 2013 SAP AG. All rights reserved. 17

Solution Components Health Platform as a Warehouse Physicians Identify clinically actionable genetic variants (e.g. causing tumor formation) in order to deliver personalized medical treatment through real-time comparison of variants to assess causal ones access to all patient-specific data anytime and anywhere Researcher Identify causal variants or mutations in cohorts (> 10,000 Individuals) suffering from diseases of interest, e.g. diabetes through comparison of variants in diseased and healthy cohorts flexible queries to verify hypotheses in real-time 2013 SAP AG. All rights reserved. 18

Solution Components Health Platform as a Warehouse Use Cases Discovery of biomarkers Clear, qualitative and quantitative results with analytics Treatment Decision Support Clinical Trial Analysis Enablers Different databases in Memory Semantics Underlying quantitative fuzzy information for further validation Side Effects Clinical Trials Individual Therapy Biomarkers SOPs Studies etc. Structured Data Unstructured Data Knowledge Graph Analytics 2013 SAP AG. All rights reserved. 19

Solution Components Knowledge Graph Sources Knowledge Graph Physician Lettersers ICD10 Codes Studies SOPs Contextualization Patient Physician Letter Concept T2 N3 M1 (TNM Classification) Her2neu (Biomarker) C50.4 (ICD10 Code) Physician Letter Study Patient Breast cancer Mamma carcinoma SOP (Standard Operation Procedure) 2013 SAP AG. All rights reserved. 20

Solution Components Knowledge Networks LEVERAGE KNOWLEDGE ORGANIZE TEAMWORK DISCOVER INFORMATION COLLECT INSIGHTS SHARE RESULTS Reuse former cases. Define work space. Bookmark from Create bookmarks. Create summary. Discover related content Invite co-researchers. anywhere or search Analyze documents. Present your findings. and information. Share best practices. Keep facts as notes. Deliver results. Breakthrough Research in Knowledge Networks Start small and scale with successes! 2013 SAP AG. All rights reserved. 21

Solution Components Decision Support A patient must not be patient anymore Dr. Vishal Sikka, SAP Board Member

Partner Solutions Decision Support Virtual Patient Platform The Virtual Patient uses molecular patient data combined with a mathematical model of tumor cells to simulate the effects of different drugs, allowing doctors to pick the optimal drug for each individual. Molecular Patient Data Drug Model Generic Cell Model Virtual Patient Model (with Alacris Theranostics & MPI for Molecular Genetics) 2013 SAP AG. All rights reserved. 23

Partner Solutions Decision Support Virtual Patient Platform IT Challenges Computational Complexity: Large mathematical problem Big Data: Many combinations of factors must be simulated for each patient (~ 10 GB / patient). Complex Analysis: Results for many drugs must be analyzed and presented in an intuitive way SAP HANA based Virtual Patient Platform Optimized model solver speeds up simulation by factor 500 All data organized in SAP HANA On-the-fly computation of result statistics; Intuitive web interface for doctors 1000s of distinct runs to overcome statistical uncertainty 1000 time steps 10 tissue types 5000 model components 10 drug doses 100 drugs 2013 SAP AG. All rights reserved. 24

Partner Solutions Decision Support Meds Clinical Clinical Data Sequencing Data Patient Case CLINICALLY ACTIONABLE KNOWLEDGESPACE Clinical Report Evidence based treatment options Other Diagnostic data 2013 SAP AG. All rights reserved. 25

Let s join Forces We can improve the lives of millions of patients around the world by building a bridge between the digital and the medical revolution Join us: hana-healthcare-platform@sap.com 2013 SAP AG. All rights reserved. 26

2013 SAP AG. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG. The information contained herein may be changed without prior notice. Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP AG and its affiliated companies ("SAP Group") for informational purposes only, without representation or warranty of any kind, and SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Group products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and other countries. Please see http://www.sap.com/corporate-en/legal/copyright/index.epx#trademark for additional trademark information and notices. 2013 SAP AG. All rights reserved. 27