IBM Healthcare Life Sciences Clinical Genomics A Path

IBM Healthcare & Life Sciences Clinical Genomics: A Path Towards Information-Based Medicine A New Era in Patient Care Kareem M. Saad WW Business Segment Executive, Clinical Genomics Information Based Medicine IBM Healthcare and Life Sciences © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Agenda Information Based Medicine: A New Era in Patient Care IBM Healthcare & Life Sciences Clinical Genomics Solution Application of Clinical Genomics Solution Implementations of Clinical Genomics Solution 2 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Emerging Business Opportunities (EBO’s) at IBM Somehow change dynamics in the marketplace § shift in business model § new set of customer requirements § disruptive technology Don’t represent business as usual § need care and feeding Create infrastructure around each EBO § Test, explore, or to morph into another opportunity 3 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences The Information Based Medicine EBO Growth Currently 1 of only 5 EBO’s across IBM Criteria Across EBO Life Cycle US$ 1 Billion+ Revenues Selection Criteria Graduation Criteria • US$ 1 billion+ potential • Strong leadership team in place • Cross–business strategic importance • Clearly articulated strategy for profit contribution • Market leadership potential • Market success • Support from business unit management Selection 4 • Proven customer value proposition Cultivation Graduation © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Healthcare and pharmaceuticals are facing unprecedented challenges driven by market forces Healthcare Drivers Aging and informed population Demand for safety and efficacy 5 Pharma Drivers Lack of R&D Productivity increase despite sustained investments Cost Pressure on prescription drugs that don’t always work Blockbuster model no longer assuring adequate sales and profit growth Increased focus on preventive care / wellness Unacceptable failure rates of R&D projects during pre-clinical and clinical development phases Progress in science and technology Serious drug side effects threatening Pharma industry Advances in information technology Need to leverage industry knowledge in patient care / disease mgmt © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Resulting in transformative changes to the delivery of and quality of healthcare Genetic testing routine for some population groups Many major diseases understood at molecular level Subpopulations at risk for adverse drug events will be identified for many therapeutics Companion diagnostics developed with targeted therapeutics Increased transition towards wellness care 6 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Automated Systems Information Correlation 1 st Generation Diagnosis Distributed High-Throughput Analytics Revolutionary Technology This transformation is being accelerated by a combination of revolutionary technologies and evolutionary practices Personalized Health Care Lifetime Treatment Pre-symptomatic Treatment CA Diagnosis Translational Medicine Molecular Medicine Genetic Predisposition Testing Clinical Genomics Health Care Digital Imaging Today Episodic Treatment Electronic Health Records Artificial Expert Systems Data and Systems Integration Non-specific (Treat Symptoms) Organized (Error Reduction) Personalized (Disease Prevention) Evolutionary Practices 7 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences The Future of Healthcare Lies in “Information Based Medicine” 8 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Information Based Medicine will require unprecedented access to diverse, integrated information Challenges • Volume and complexity of data • Integrating massive volumes of disparate data • Need for sophisticated analytics • Growing collaboration across ecosystem 9 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Academic Medical Research Centers & “Biobank” Challenges • Medical Research Care Delivery Patient • • • Academic Medical Research Centers (AMRC’s) and Biobanks • focus heavily on the integration of care delivery and research • functions to improve understanding of disease, improve quality of care and • ultimately move towards personalized medicine 10 Collect, access or mine all patient data (including blood and tissue samples) securely for research Patient records not well organized – difficult to find patient history/need to build enterprise clinical data warehouses Compliance with regulatory requirements, particularly patient privacy regulations (e. g. HIPAA in US) Emerging role of genomics requires patient records to be enhanced with new molecular profiling data Leverage data standards to enable linkages and associations between disparate data types (e. g. phenotype and genotype) IT departments not equipped to deal with complexities of the data integration challenge faced by translational research Establish leadership position to attract gov’t and industry funding as well as attract top medical researchers © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Biopharmaceutical R&D Challenges • Pharma Discovery Clinical • Development • Bio-Pharmaceutical R&D organizations are adding targeted treatment capabilities requiring investment in new technologies and IT infrastructures for blood and tissue sample management and patient databases that incorporate genotypic data, and query, analysis, and mining capabilities. 11 • • • Identify and validate specific drug targets associated with chosen therapeutic areas Collect, access or mine patient data (including blood and tissue samples) securely for research Recruit patient populations characterized by Biomarkers identified by diagnostic tests “Rescue” drugs that failed due to side effects in subsets of patient populations characterized by shared Biomarkers Manage blood and tissue samples collected during clinical trials Compliance with regulatory requirements, particularly patient privacy regulations (e. g. HIPAA in US) Leverage data standards to enable linkages and associations between disparate data types (e. g. phenotype and genotype) Pharma IT departments must deal with complexities of the data integration challenge faced by need to combine patient data with genomics, biomedical image and literature data © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Agenda Information Based Medicine: A New Era in Patient Care IBM Healthcare & Life Sciences Clinical Genomics Solution Application of Clinical Genomics Solution Implementations of Clinical Genomics Solution 12 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Clinical Genomics – What is it? “Clinical genomics is the marriage of large scale technologies for molecular analysis with the study of actual disease. ” -Cambridge Healthtech Institute Different names intended to describe the same thing: • Clinical Genomics • Medical Informatics • Pharmacogenomics • Personalized Medicine • Healthcare Informatics • Translational Medicine • Medical Genomics or Genomic Medicine • … and on!!! 13 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Clinical Genomics: Conceptual Overview 14 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences IBM Healthcare and Life Sciences Clinical Genomics Solution Conceptual Architecture Medical Research Expression, SNPs, Clinical Studies & Trials, Proteomic Clinical Care HIS, RIS, CIS, Pathology, Rx, Patient Charts Data Mining/Statistical Analysis/Visualization Adherence to Standards HL 7, BSML/Hap. Map, CDISC/ODM, MAGE-ML, CDA, etc. Medical Information Gateway Deidentification of Patient Data & Anonymous Global Patient Identifier Assigned Medical Information Broker 15 Web. Sphere DB 2 Information Integrator Medical Information Repository Source scientific data & unstructured text files e. g. MS Access. MS Excel, EST/ Gen. Bank, XML, Medline, db. SNP © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences The Clinical Genomics Solution enables multiple institutions to seamlessly and securely share information for collaborative research and improved clinical care MIG MIB Data Mining/Statistical Analysis/Visualization Web. Sphere 16 MIG MIB DB 2 Information Integrator Medical Information Repository Source scientific data & unstructured text files © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences SNP Clinical Studies Gene Expression Patient Charts CDA Open Source Relational Connect de. Code CGM-D Pub. Medical Information Repository DB 2 Flat File / XML DB 2 II Correlation / Curation CDA, MAGE-ML, BSML / Hap. Map, ODM … Web. Sphere De-Identification Medical Information Broker Spot. Fire Web Services BLAST Anonymous Global Patient ID … SAS Data Handlers DDQB CDA Constructor Parser / Loader AGPI Server Data Handler Micro Strategy Optional Applications Search, Query, Mining, . . Medical Information Gateway 17 Researchers ODBC Medical Information Administration Service CDISC / ODM MAGE-ML HL 7 2. x Interface Engine BSML / Hap. Map HIS, CIS, RIS, Pathology, Rx CG V 2 Architecture MS Access / Excel EST / Gen. Bank Delimited / XML Files Medline db. SNP © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Medical Information Gateway (MIG) Phase I: Enablers & Inhibitors Medical Information Gateway 18 Functionality • Interfaces with systems in the hospital and research settings to capture, normalize, de-identify and aggregate information based on each patient or research encounter • Captures diagnostic, clinical, demographic, gene expression, clinical study, phenotypic, life style or environmental data from a range of lab or hospital systems (e. g. CIS, HIS, RIS) • Transforms information into the appropriate data standards, deidentifies the data according to local regulatory requirements and assigns an anonymous global patient identifier Benefits • Securely de-identifies patient data to ensure adherence to regulatory requirements (customizable using administrative interface) • Multiple Gateway’s can be deployed at disparate sites or departments, enabling secure, cross-institutional collaboration • Ability to interface with multiple existing hospital and research systems ensures that existing IT investments are leveraged • Adheres to industry and open standards ensuring future flexibility and extensibility of the integrated research and clinical environment © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Medical Information Repository (MIR) Phase I: Enablers & Inhibitors Medical Information Gateway 19 Functionality • Integrated data store of all the phenotype and genotype information captured by the Medical Information Gateway and delivered through the Medical Information Broker • Solution can be deployed with a single MIR serving as a large data warehouse or with multiple MIRs accessible through DB 2 Information Integrator’s federated technology • Leverages IBM’s robust and secure DB 2 technology Benefits • Accelerates understanding of complex diseases at the molecular level by providing an integrated environment for the analysis of large sets of genotypic and phenotypic information • Multiple third-party, open source or proprietary data mining, visualization and analytics can be used to query and mine the MIR due to adherence to open and industry standards • Links clinical and research environments seamlessly and enables secure information and secure data sharing across departments and institutions © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences DB 2 Information Integrator Search Find Federate SQL Content Transform Place Publish DB 2 Information Integrator Shared metadata and access foundation Phase I: <XML> Text </XML> Enablers & Inhibitors Functionality DB 2 Information Integrator • Real-time, integrated access to business information – structured and unstructured, mainframe and distributed, public and private, IBM and non-IBM – across and beyond the enterprise • Clinical Genomics-specific support: access structured scientific data and unstructured text files via federation, replication, triggered data publication, and enterprise search capabilities Benefits • Integrate information across multiple business processes • Get more value and insight from existing assets • Control IT costs by tailoring views and reducing Any data redundancy Multiple access paradigms Multiple integration disciplines 20 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences IBM Healthcare and Life Sciences large ecosystem of partners* across the clinical and research domain, helps to ensure seamless integration and minimal disruption to operations * NOTE: Not a comprehensive list 21 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Agenda Information Based Medicine: A New Era in Patient Care IBM Healthcare & Life Sciences Clinical Genomics Solution Application of Clinical Genomics Solution Implementations of Clinical Genomics Solution 22 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences A major University Health System consists of the primary University Hospital, 5 affiliated regional hospitals and a large cancer center University Hospital • 400 bed hospital w/ over 20 K patients per year • Clinical and imaging systems not integrated • Participation in multiple clinical trials Affiliated Hospital Centers • 5 affiliated health centers • Clinical systems loosely integrated with University Hospital • Participation in multiple clinical trials sponsored by University Cancer Center • University Cancer Center affiliated with Hospital • Large genotyping and molecular profiling capabilities • Receives substantial government funding for basic research 23 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences The University Health System implemented the Clinical Genomics Solution to integrate genotypic, phenotypic, clinical trials and patient data from each hospital and research site in network Data Types Clinical trials (ODM), Clinical (ODM), Imaging MIG MIB Data Mining/Statistical Analysis/Visualization De-identification Web. Sphere Data Types Expression (MAGE), Mutation (BSML), Genotype (BSML) MIG MIB DB 2 Information Integrator De-identification Data Types Clinical trials (ODM), Clinical (ODM), Imaging MIG De-identification 24 MIB Medical Information Repository Source scientific data & unstructured text files e. g. db. SNP, Pub. Med, Gen. Bank © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences The integrated system enabled collaboration between a now networked clinical and research environment Scenario § Hospital has been running a large, pharmaceutical sponsored clinical trial for lung cancer § Mechanism of action of the compound is not precisely known and a limited sub-set of patients are showing efficacy § Several patients enrolled in study are experiencing side effects § An oncologist involved in the trial has discussed this with an oncology fellow and asked the fellow to investigate if the adverse effects or compound mechanism of action can be identified Data Types Leveraged in Scenario § Clinical Data (CDA) § Gene Expression (MAGE) § Clinical Trial (ODM) § Mutation Data (BSML) § Genotype (Hap. Map) § Unstructured Text (Pub. Med) § db. SNP 25 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Research fellow queries de-identified genomic and clinical information in MIR to identify all lung cancer patients at network of medical centers Deidentified, anonymous global patient identifier 26 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Research fellow able to identify lung cancer patient of interest without revealing identity because of deidentification 27 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Fellow is able to query Pub. Med through same interface as MIR queries leveraging DB 2 Information Integrator Paper indicates poor survival rates for patients with lung tumors containing K-ras mutations. 28 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Oncology fellow queries clinical, gene expression & mutation information simultaneously in MIR for patient X Fellow is able to determine that patient X shows SNP at amino acid 12. 29 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Leveraging DB 2 II, the fellow is able to seamlessly query db. SNP to determine if the mutation is already reported in the public DB Location of SNP for K-ras 2 protein in db. SNP is at the 161 amino acid position. Patient X shows SNP at amino acid position 12 – this could be a publishable finding. 30 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Fellow then queries MIR to determine if patient x is enrolled in any clinical trials at the medical center Fellow is able to determine that the trial includes chemotherapy agent Doxorubicin Hydrochloride and Carboplatin. 31 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Fellow then queries clinical trials data for vital signs and patient information in the trial Fellow is able to examine patient’s vital signs as well as tumor information. Determines tumor is moderately differentiated. 32 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Oncology fellow then queries clinical information in Medical Information Repository to see deidentified clinical history for patient X Patient X clinical history shows that he has history of heart problems. 33 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Levering DB 2 II, the fellow queries Pub. Med recognizing that due to patient X’s history of heart disease attention must be given to hypersensitivity reactions exhibited by anti-tumor agents Paper indicates that Carboplatin has a role in coronary vasospasm. The paper concludes that cancer patients collapse when given 450 mg or more of Carboplatin to treat their tumors. 34 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Fellow queries MIR to access clinical trial data to determine amount of Carboplatin administered to patient X Fellow is able to determine that level of Carboplatin administered during trial is likely non-toxic. 35 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Fellow queries for another patient in the clinical trial who has been given a similar dosage as patient X 36 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Fellow queries for patient Y’s clinical phenotype information to compare with patient X. Patient Y, who is enrolled in the same trial and has been administered similar dosage in trial, does not show signs of heart disease like patient X. 37 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Fellow queries MIR for genotypic information on patient X and patient Y Patient X Patient Y He predicts that the phenotypic differences seen between these 2 patients who are on the same drug trial could possibly be due to the genotypic difference. 38 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Agenda Information Based Medicine: A New Era in Patient Care IBM Healthcare & Life Sciences Clinical Genomics Solution Application of Clinical Genomics Solution Implementations of Clinical Genomics Solution 39 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Recent Clinical Genomics Projects: University of California San Francisco Challenge: Accelerate development of pharmacogenomic treatments by integrating diverse data in clinical research. Focus: Neurology, Cardiology and Oncology. Solution: A Clinical Genomics Information Mining system to facilitate the simultaneous mining/query/analysis/ and cross association of all of the data generated by the clinician researcher and the associated clinical data from the hospital. Benefits: The system will depict associations within the clinical and research data that helps the researcher formulate or explore new hypotheses of the genetic and biological basis of disease. This will enable more accurate diagnoses and tailored treatment, resulting in improved patient outcomes. 40 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences University of California, San Francisco (UCSF) Differential Diagnosis of Dementia - Challenge “Gold standard” for diagnosis = Pathology Patient Dementia diagnosis Alzheimer’s Rapidly-progressing dementia Non Rapidlyprogressing dementia Is there a combination of clinical, pathological, molecular and submolecular “markers” that would allow the better and earlier diagnosis of various forms of dementia? 41 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences University of California, San Francisco (UCSF) Differential Diagnosis of Dementia - Architecture 42 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences University of California, San Francisco (UCSF) Long-term Strategy Disease Area Multiple Sclerosis Dementia Clinical Development Discovery Institution #1 Institution #2 Functional Focus (as part of the information-based medicine continuum) Institutions (internal and external stakeholders) 43 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences H. Lee Moffitt Cancer Center and Research Institute Background Largest specialty tertiary cancer center in Florida (30% of all cancer patients in the state of Florida covered by Moffitt Network) NCI designated Comprehensive Cancer Center § Patient Care § Clinical and Basic Research § Education (residency programs and affiliation to USF) In-patient functions, out-patient functions, SE largest Bone Marrow and Transplant program and Lifetime Cancer Screening Mandate “Translational Research”: focused on the rapid translation of scientific discoveries to better patient care 44 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences H. Lee Moffitt Cancer Center and Research Institute Problem Being Faced Lung Cancer patients A single treatment regimen of both chemo and radiotherapy Responders Non-Responders Lung Cancer patients Differential diagnosis based on known AND unknown disease markers 45 Responders Treatment Regimen #1 Responders Treatment Regimen #2 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences H. Lee Moffitt Cancer Center and Research Institute Solution Hospital Information Systems Clinician Scientist Technician 46 Electronic Medical Record FDA Submission Pathology Laboratory Case Report Forms Research Data • Gene Exp • Sequence • Proteomics Information Integration • Data Warehousing • Data Federation Data Analysis • Open Standard Representation • Regulatory Compliant Implementation Feedback into clinical development © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences IBM/Mayo Clinical Genomics Collaboration Phase I: Example of a real-time query Find all patients with: § § § § Coronary artery disease (a form of heart disease) Diabetes Mellitus (“diabetes”) Nonalcoholic steatohepatitis (a form of liver disease) Who had a breast biopsy at Mayo (a procedure) In ZIP code 55901, 55902, 55903, 55904 (local region) Between 45 and 65 years of age (certain age) Who are female (female gender) And are alive (vital status) Before After 47 6 Weeks 6 Seconds © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences IBM/Mayo Clinic Collaboration Applied Genomics Data Analysis Genomic data (DNA) – Gene. Chip array data (RNA) Protein data Clinical Data Signs Symptoms Laboratory Radiology Etc. Phase I 48 Databases Genome Proteome Disease Tumors Drugs Optimized, individualized healthcare © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences During Phase I, the Mayo Clinic Partnership has produced one of the world’s largest Clinical Data Warehouses Warehouse contains over 4. 4 Million Patient Records Infinite number of unique queries across § 28 demographic elements § 523 DRG codes § 10, 455 ICD-9 codes §All structured laboratory test conditions or results (up to 4900+) §All microbiology organisms by name; heart rate on ECG Mayo researcher benefit – months to minutes time savings for select cases tested 49 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences In Phase II, Mayo and IBM will focus on Genomic Data and Text Analysis 50 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Example of Query … able to run at end of Phase II “Find all living female patients with diabetes with a good quality microarray experiment” § Diabetes Mellitus (Diagnosis Codes, Medical Index & Clinical Notes) § Serum Glucose > 150 mg/d. L (Results) § Microarray data exist (Storage & Retrieval) } c. RNA labeling efficiency > 90% (Workflow) § Between 45 and 65 years of age at first diagnosis (Demographics combined with Diagnosis Codes, Medical Index & Clinical Notes) § Who are female and alive (Demographics) 51 IBM and Mayo Confidential © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Phase II Solution Architecture DDQB HSR SAS R S-PLUS Spotfire Bioconductor Affymetrix Genes@Work Laboratory Results and Reports Registration, RAS, Diagnoses Codes and Medical Index Clinical Notes Gene Expression Gene Sequences Length Polymorphs Researchers Search, Analyze, Export Data and Results Web Services 52 DB 2 Document Driver Annotators Standard or New XML format Shredders MAGE-ML Standard or New XML format DDQB reference data JEDII CDA / XML Replication Golden. Gate ETL XML transform XML transform Websphere Life Sciences Warehouse CAS Extraction © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences IBM’s Commitment to Healthcare and Life Sciences IBM Healthcare and Life Sciences § Aligned Clinical Environment } Healthcare Collaborative Network } Patient Centric Healthcare Portal } Clinical Decision Intelligence } Clinical genomics § Safe and Lean Hospital IBM Business Consulting Services § Component Business Model § IT/business consulting IBM HC and LS Development § Teams of IT experts and SMEs § Integrating standards § Data Discovery and Query Builder IBM Hardware, Software, Middleware § Servers, Storage, Tivoli, Websphere, etc. 53 IBM Partners who provide Applications § § § Clinical and Enterprise Information Systems Patient Accounting Systems Admitting Systems Patient Care Systems Ancillary Systems: Laboratory, Pharmacy, Radiology § Physician Office Systems § Life Sciences partners IBM Strategic Outsourcing IBM Research § Med II } IMR – integrated medical records } GMS – Genomic messaging system } Prima – patient record intelligent monitoring and analysis © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Thank You Q/A Discussion 54 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences BACK UP SLIDES 55 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Clinical Genomics: Data Flow 56 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Clinical Genomics: The Good News…Early Results There are early and encouraging signs of new drugs and diagnostic tests coming from a deeper understanding of genomics – The first approved drug to come from an understanding of gene expression was Gleevec from Novartis. It is used to treat chronic myeloid leukemia (about 20% of all adult leukemia) and had sales of $615 M in 2002. Another example is Genentech’s Herceptin treatment for breast cancer which is an early example of personalized medicine § Herception only works in women who have multiple copies of the Her-2/neu gene; approx. 25% of all breast cancer patients. Sales in 2002 were $285 M. Other examples of how a deeper understanding of genomics can assist in diagnostics and therapeutics § Cerezyme, a drug for Gaucher disease found in Ashkenazi Jews with a specific genetic mutation. A DNA test can identify this and indicate preventative treatment. § Lotoronex, a drug for irritable bowel syndrome which was pulled from the market because of side effect issues. GSK is looking at a diagnostic test which would predict those patients susceptible to the side effects…otehrs could be treated successfully. Source: Life Science Insights, 2004 57 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Why IBM and Affymetrix? Characteristics of an Information Technology Partner § Integrated solution provider with sophisticated hardware, software and services capability § Leadership in middleware technology to help set and propagate standards § Extensive partner ecosystem in both Healthcare and Pharmaceutical research, and compatibility with their applications § Vision and solution for information technology challenges for entire personalized medicine pipeline § Domain expertise in data integration and management and regulatory compliance (HIPAA, 21 CFR Part 11, etc. ) § Scalable, open standards based solutions that leverage existing investments in research and clinical IT 58 Characteristics of a Genomics Technology Partner § Platform available today and extensible to the future § Support for expression, genotyping and resequencing § Supports cost effective high throughput applications § Scalable from whole genome analysis to focused clinical setting § Standard for the community § Regulatory “ready” § Path to commercial diagnostics § Proven leadership position in platform technologies © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences Information Based Medicine: The Building Blocks The common thread for these pieces is the necessity of data acquisition, management and analysis to improve diagnostic decisions and patient outcomes. 59 © Copyright IBM Corporation 2004

IBM Healthcare & Life Sciences The Future of Healthcare lies in Prevention and Personalization Hospital Baby Secure database Genotype recorded Bio. Bank Physician’s office Patient scenario in the future • Improve quality of life • Increase life span Personalized immunization + Screening schedule + 60 profiling Preventive measures Refined treatment Sources: Science, Mc. Kinsey Screening based on biomolecular Drugs Life Adult Developed to target particular disease subtypes on particular genetic background © Copyright IBM Corporation 2004