This copyrighted presentation was made at the Bryn Mawr Innovation Systems Biology Conference, October 14, 2008.

1. ITHW, Inc. Innovative Technologies in Health and Wellness David S. Lester, Ph.D. President, ITHW, Inc. Executive VP, Gene Express, Inc. October, 2008 A Systems Approach to Identifying Technology Interventions Based on Patient-Centered Outcomes

5. The Key Is Identifying and Integrating Patient-Centric Technology Strategies to Prove Value to Multiple Stakeholders Payers Suppliers Employers Providers Patients Regulators Caregivers The Perception of Value Depends on Stakeholder Perspective Pharma ITHW, Inc. Innovative Technologies in Health and Wellness

6. The Healthcare System of Today: Which iPod do I trust? Regulators Pharmaceutical Companies Payers Providers Suppliers Caregivers Device Manufacturers Employers ITHW, Inc. Innovative Technologies in Health and Wellness

7. The Patient of Today – The iPod: What accessory do I choose? Eastern Medicines /Treatments Generics Pharmaceuticals Supplements Regulated Devices Non-Regulated Devices Nutraceuticals Physical Activities ITHW, Inc. Innovative Technologies in Health and Wellness

8. Developing Patient-Centric Technology Strategies Adding Value by Optimizing Key Points of Patient Impact Expanding Opportunities Across the Cycle of Patient Care Diagnosis Intervention Adherence Control Improved Outcomes Redefining & Identifying Diseases for Product Development Redefining Performance & Execution of Clinical Value Product Superiority/ Inferiority Novel Information Individualized Monitoring Pricing ITHW, Inc. Innovative Technologies in Health and Wellness

10. Systems Dynamics Modeling of the Diabetic Patient Outcomes ITHW, Inc. Innovative Technologies in Health and Wellness

11. We are utilizing a proven method that enables us to integrate a wide range of dynamics and stakeholders Introduction to System Dynamics . . . ITHW, Inc. Innovative Technologies in Health and Wellness

12. MIT research shows that beyond three interacting feedback loops, intuition and conventional analysis break down Debt & Equity Passengers Flown Physical Capacity Service Capacity Product Attractiveness Shareholder Value Cause-effect relationships close in on themselves to form feedback loops – interacting feedback loops generate performance over time Introduction to System Dynamics . . . ITHW, Inc. Innovative Technologies in Health and Wellness ? ? Earnings Revenue (Unit Sales) Service Quality Customers Ability To Raise Capital Ability to Attract & Hire Employees

13. The complexity of diabetes and its C&Cs is reflected in their extensive interacting feedback loops Introduction to System Dynamics . . . ITHW, Inc. Innovative Technologies in Health and Wellness Atherosclerosis Obesity Stroke CHD Diabetes Depression Dyslipidemia Retinopathy Neuropathy Nephropathy Hypertension

14. Our second step was to organize diabetes and its complications and comorbidities into ten groups for the PoP effort Fasting Plasma Glucose (FPG) levels at presentation Type 2 Diabetes Diabetic FPG 126-299 mg/dL Non-diabetic FPG <100 mg/dL Pre-diabetic FPG 100-125 mg/dL Severe State Moderate State Non-state Pre-state Complication or comorbidity Specific classification index Obesity Coronary Heart Disease Stroke Atherosclerosis Dyslipidemia Hypertension Depression Nephropathy Neuropathy Adult 45-64 Adult 20-44 Adult 65+ Retinopathy When we distinguish three age groups, the number of groups triples from 120 potential patient pools to 360 potential patient pools Clinical landscape inventory . . . ITHW, Inc. Innovative Technologies in Health and Wellness

16. Example of a complication of diabetes: Nephropathy Nephropathy * National Kidney Foundation/Kidney Disease Outcome Quality Initiative (NKF/KDOQI) classification system Complication/Comorbidity Clinical landscape inventory . . . Further details are provided in the supporting C&Cs Appendix ITHW, Inc. Innovative Technologies in Health and Wellness Demographics/Epidemiology Contributing factors Diagnostic Disease State Classification* Downstream Outcomes Sample Outcome Approximately 25-50 % of Type II DM patients will develop kidney disease, although do not present with symptoms until 5-10 years post onset of disease. Patients from an Asian or Afro-Caribbean origin are twice as likely to develop diabetic kidney disease. Diabetic nephrology accounts for approximately 40% of all cases of new end stage renal disease (ESRD). Hypertension, Atherosclerosis, Neuropathy. Severity of condition depends upon comorbidities of patient. Hyperglycemia and exposure to a high protein diet are important risks for development of proteinuria. Albumin (urine sample, first passing of day), creatinine (blood sample) Microalbumin-uria (marker of development of nephrology) -albumin levels over 30mg in 24h. Macroalbumin-uria (marker for progression to Stage V: ESRD)- albumin levels above 300mg. Serum creatinine levels outside normal range 0.8-1.3mg/dL indicates major kidney functional loss. Assessment of findings provide clues to stage of renal disease: Stage I: Time of diagnosis. Kidney size is increased. Glomerular Filtration Rate (GFR) is >90ml/min/1.73m 2 . Reversible by blood glucose control. Stage II: 2-3 yrs post-diagnosis. Glomerular basement membrane thickens and decline in renal function initiated. Scar tissue formation occurs. GFR 60-89ml/min/1.73m 2 Stage III: 7-15 yrs post-diagnosis. Microalbuminuria first appears. Glomerular damage has progressed and hypertension may be present. Patients are asymptomatic. GFR 30-59ml/min/1.73m 2 Stage IV: Overt, or dipstick positive, diabetes. Almost all patients have hypertension. Suboptimal glucose control. GFR 15-29ml/min/1.73m 2 Stage V: ESRD; GFR <15ml/min/1.73m 2 Renal replacement required. Coronary heart disease (due to macroalbu-minuria), Kidney failure

20. Addition of technology impact points for all the diabetes populations and one C&C (obesity) expands the complexity OBESITY DIABETES Model structure . . . ITHW, Inc. Innovative Technologies in Health and Wellness NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality

21. The complexity is further enriched when the other C&Cs are added Model structure . . . ITHW, Inc. Innovative Technologies in Health and Wellness

24. When the simulation combines medical and epidemiological facts, interesting potential insights emerge For each impact point, the difference between the base case and an ideal situation (ie, no progress, no control problems, total adherence) was reduced by 50%. Here only the most influential half of impact points are shown. Technologies acting on which impact points would have the most influence*? Metric = Deaths averted compared to base case from 2008-2025 Not surprisingly, progression has the biggest impact on mortality. Due to the fact that even Pre-Diabeties significantly increases the risk of death in Older Adults, and that many pre diabetics become diabetics, preventing the onset of Pre-Diabeties has an even bigger impact than preventing the onset of full blown diabetes Keeping Non-diabetics from progressing to Pre-diabetics in general has more of an impact than keep Pre-diabetics from progressing to Diabetes because of the relatively larger Non-diabetic population, because Pre-diabetics exhibit some of the problems Diabetics Exhibit, and cutting down on Pre-diabetic development ultimately also decreases diabetes. Following Progression, Control and Adherence have the biggest impacts. While Progression of Non-diabetics to Diabetics has a larger impact than the progression of Pre-Diabetics to Diabetics, glucose control of diabetics has a larger impact than control of Pre-diabetics. Reference Point: 1 million deaths averted represents about 2% of the 50M total US deaths in the base case from 2008-2025. ILLUSTRATIVE RESULTS ONLY Model results . . . * “Progression, Non Diabetic, Older Adult” represents the progression of older, non-diabetic adults to older, pre-diabetic adults ITHW, Inc. Innovative Technologies in Health and Wellness

25. Adding Additional Patient Subgroups ITHW, Inc. Innovative Technologies in Health and Wellness

26. Preliminary categorization has identified several high-priority sub-groups for incorporation into the model e.g., Particular obesity treatments are not suitable for children e.g., Treatment efficacy might be influenced by sex hormones e.g., An anti-smoking treatment will not affect non-smokers * For additional information, see Appendix A: Supporting details for sub-group classification ** Priority ranking: 1 = highest 1 2 Considerations . . . ITHW, Inc. Innovative Technologies in Health and Wellness Smokers Age Gender Time with diabetes Alcohol consumers Ethnicity Genetic pre-disposition For how many C&Cs are the sub-groups relevant? 8 6 5 3 7 8 6 Is the prevalence of diabetes & its associated C&Cs altered among the different sub-groups of the classification? Yes Yes Yes Yes Yes Yes Yes Do differences exist in the Relative Risks (RRs) among the different sub-groups of the classification? Yes Yes Yes Yes Unclear Unclear No Suggested priority 1 1 1 1 2 2 3 Is a difference in technology impact among the different sub-groups of the classification likely to be observed? Yes Yes Yes Yes Unlikely No Unlikely Revised priority with consideration of the technology impact consideration 1 1 1 1 2 3 3

27. While expanding further patient sub-groups adds granularity to the model, the increased complexity must be managed Obesity Coronary Heart Disease Stroke Atherosclerosis Dyslipidemia Hypertension Depression Nephropathy Neuropathy Retinopathy Diabetic Non-diabetic Pre-diabetic Severe State Moderate State Non-state Pre-state Adult 45-64 Adult 20-44 Adult 65+ Adding only 2 further sub-group classifications across all C&Cs, each with 4 sub-groups, significantly increases complexity 4x3x3x10 = 360 “slices” in the model 4x3x3x10x(4x4) = 5760 “slices” in the model PoP (v1.0) Model PoP (v1.1) Model Considerations . . . ITHW, Inc. Innovative Technologies in Health and Wellness Obesity Coronary Heart Disease Stroke Atherosclerosis Dyslipidemia Hypertension Depression Nephropathy Neuropathy Retinopathy Diabetic Non-diabetic Pre-diabetic Severe State Moderate State Non-state Pre-state Adult 45-64 Adult 20-44 Adult 65+

28. Addition of Adherence Factors ITHW, Inc. Innovative Technologies in Health and Wellness

33. Total Population very closely matches historical estimates Younger Adults Middle Aged Adults Older Adults Simulation Historical Data Step #1: Historical Calibration… ITHW, Inc. Innovative Technologies in Health and Wellness Total Population by Age Group 1995.0 1996.8 1998.6 2000.4 2002.2 2004.0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000

34. THANK YOU Partners: PA Consulting Joe Alexander, Pfizer Human Health Technologies ITHW, Inc. Innovative Technologies in Health and Wellness