Enabling the Future of Care Delivery: IT-Driven, Molecular Medicine

HIMSS Annual Conference

February 27, 2007

Lyle Berkowitz, MD

Keith Strier, JD, PAHM

“To wrest from nature the secrets which have perplexed philosophers in all ages, to track to their sources the causes of disease, to

correlate the vast stores of knowledge, that they may be quickly available for the

prevention and cure of disease — these are our ambitions.”

—Sir William Osler (1849 – 1919)

Our Ambitions

Agenda

Background

The Great Integration

Implications and Future Thinking

The PromiseImagine when doctors can…

Predict Disease pre-symptomatically with simple testing

Prevent Disease by identifying risks, early interventions

Diagnose Conditions less invasively, more accurately

Select Drugs that maximize benefits and minimize risks

Calibrate Treatments to heighten efficacy and recovery

Treat/Cure Disease using our own genes

Dis

ease

Bur

den

Time

Cos

t

1/re

vers

ibili

ty

Typical Current

Intervention

Earliest Clinical

Detection

Earliest Molecular Detection

Initiating Events

Baseline Risk

Decision Support Tools:

Baseline Risk Preclinical Progression

Disease Initiation and Progression

Assess Risk Refine Assessment

Predict

Diagnose

Track Progression

Predict Events

Inform Therapeutics

Sources of New Biomarkers:

Stable Genomics: Single Nucleotide Polymorphisms Haplotype Mapping Gene Sequencing

Dynamic Genomics: Gene ExpressionProteomics Metabolomics Molecular Imaging

Therapeutic Decision Support

Sou

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Changing Paradigm of Care

Real World Examples

Stable Genomics (Inherited Genes)– BRCA 1 & 2 predictor of breast and ovarian cancer risks– LDLR and APOB predictor of developing early coronary artery disease– MODY 1-6 predictor of MODY diabetes; subtypes affect treatment choice– CYP2D6/C19 Main cytochrome P450 genes that affects drug metabolism dosing – CYP2C9/VKORC1 variants in these cP450 genes affect warfarin metabolism– TPMT guides adjustment of Purinethol dosing in Acute Leukemia patients

Dynamic Genomics (Gene Expression, Biomarkers…)– Estrogen Receptor predicts response to Tamoxifen in breast cancer– HER-2 Receptor predicts response to Herceptin in breast cancer– PSA predicts risk of prostate cancer– Cholesterol predicts risk of heart disease and strokes– HIV Genotyping to guide selection of therapy– PET Scans to diagnose and help manage treatment options for various cancers

Evolving Applications

Open Source Tools/Networks

Understand

Apply

The Great Integration

Genetic Banking

Clinical Collaboration

NUGene Project Understanding Genomics

NUGene: Data Flow & Privacy

NOTISNUgene

DatabasePhenotypic

Data Warehouse

Coded Data

Phenotypic Engine

Encryption

Decryption

De-identification Process

Medical Record

Participant Enrollment Materials

Patient Identifiers

Clinical Proteomics Initiative Understanding Proteomics

Background– Joint initiative between the NCI and the FDA – Correlate protein and gene expression patterns

Early detection and cancer screening Establish therapeutic response endpoints Monitor drug toxicity during treatment

Ovarian Cancer Project– Phase 1: Identify diagnostic patterns– Phase 2: Confirm diagnostic ability– Phase 3: Test in real world

Correlagen Applying Genomics in the Real World

Genes That Matter™ Integrated Results Reporting Examples

– Maturity-Onset Diabetes of the Young (MODY)– Early-Onset Coronary Heart Disease– Severe Combined Immunodeficiency (SCID)

Case Scenario #1

3yo male Acute lymphoblastic leukemia Being treating with mercaptopurine

How do you currently manage this scenario?

How will you manage this in the world of molecular medicine integrated with EMRs?

Pharmacogenomic Alert

Pharmacogenomic AlertThis patient has a TMPT gene defect which indicates a high

sensitivity to standard doses of mercaptopurine.

[] Cancel Drug Order

[] Lower the standard dose (75mg/m2) by 85% to a modified dose (11.25 mg/m2)

[] Ignore this Alert

Case Scenario #2

32yo Female New onset diabetes (non-ketotic) Non-obese

How do you currently manage this scenario?

How will you manage this in the world of molecular medicine integrated with EMRs?

Diagnostic Alert

Dx AlertThis patient fits the profile for MODY. Consider

checking for MODY genetics

[] Order MODY Genetic Screen

[] Ignore Alert

[] Learn more about MODY

Treatment Alert

Treatment AlertBased on known genomic data and phenotype

expression in this patient, the best treatment for their Type 1 MODY diabetes is to start with a sulfanurea.

[] Change Order to a Sulfanurea

[] Ignore Alert

ImplicationsThings to start thinking about

Diagnose

Treat

PredictCDS

PersonnelStorage

Impact on Other Players

The Future

Technology Advancements– The $1000 Complete Genome– BioMarker Testing: POC/Continuous– Superior Molecular Imaging

Ethical, Financial and Regulatory Issues– Who should get these tests– Who should pay for these tests– How should this data be stored– How will this data be used

Better,

Faster,

Cheaper

Resources

The Human Genome Project: www.genome.gov The NUGene Project: www.NUGene.org Clinical Proteomics Project: http://proteomics.cancer.gov The FDA and Genomics: www.fda.gov/cder/genomics The CDC and Genomics: www.cdc.gov/genomics/default.htm NIH & Pharmacogenetics: www.nigms.nih.gov/Initiatives/PGRN Non-Profit Organizations

– http://bioitalliance.org – http://www.personalizedmedicinecoalition.org

News and Updates– http://www.ageofpersonalizedmedicine.org – http://www.genomenewsnetwork.org/

Thank You