Using Virtual Population Simulation to Generate Evidence for Reimbursement Discussions

1. Using Virtual Population Simulation to Generate Evidence for Reimbursement Discussions Generating Evidence for Reimbursement Decisions Conference Badri Rengarajan, MD November 6, 2012

2. Today’s Objectives Understand how virtual population simulation can help inform reimbursement discussions Provide an overview of the Archimedes Model Review case studies

3. Topics • Development, Commercialization and Simulation Modeling • Overview of Archimedes Model • Case Study: Lynch Syndrome • Closing Thoughts • Q&A • Appendix: • Case Study: DPP Trial Expansion and Extension (ADA, DHHS) • Illustration: ARCHeS desktop simulation tool

4. Development and commercialization are challenging Illustrative Commercialization Development Preclinical & Ph1 Ph2 Ph3 Registration Payor Commercial PMCs • Poor prediction of downstream efficacy/safety • Suboptimal patient targeting • Inadequate powering: Uncertainty around baseline event rate • Synchronizing CDx and Tx development • Recruiting challenges • Lack of clarity on reg path • Delay • Rejection • Large N for Ph4 study • Lack of data in real-world settings • Comorbidities • Poor compliance • Poor adherence • Comparative effectiveness • Competition/Head-to-head • Change in SOC • Fitting into current clinical workflow

5. Reimbursement is challenging, especially in diagnostics • “Chicken-egg” situation • Payors want to see outcomes and data from real-world settings, which will take time to generate • …Yet reimbursement is set today • Coming back later with data in hand unlikely to change reimbursement • Financial base of Dx companies much smaller than therapeutics companies – thus cannot conduct several outcomes and post-approval-type studies • How to price? • Is Genomic Health’s OncotypeDx my best proxy? • How to avoid getting slotted into precedent stacked codes? • For CDx, what is the Dx value in context of Tx? • How can I build a compelling case for payors?

6. What To Do Now? Fed intervention Four-leaf clover Crystal ball Rain dance

7. Simulation modeling is already used in several areas

8. Topics • Development, Commercialization and Simulation Modeling • Overview of Archimedes Model • Case Study: Lynch Syndrome • Closing Thoughts • Q&A • Appendix: • Case Study: DPP Trial Expansion and Extension (ADA, DHHS) • Illustration: ARCHeS desktop simulation tool

9. What comes to mind when we hear“virtual population simulation”? PK/PD simulation Monte Carlo simulation Mannequins Telemedicine Source: Google Images; Healthcare IT News (April 12, 2012)

10. What is Archimedes“virtual population simulation” not? • “ Virtual” • Not “virtual R&D” or outsourced clinical trials • Not telemedicine • “Simulation” • Not PK/PD simulations • Not Monte Carlo simulation for enrollment or marketing • Not mannequins

11. What is Archimedes virtual population simulation? • Industrial-strength, full-scale modeling • Playing out the lives of thousands of trial subjects approximating real people, without recruiting a single live person • Captures physiological/disease outcomes and healthcare system interactions, including patient/provider behaviors • Across different populations • Across different possible trial protocols • Across different healthcare systems and cost frameworks • Supplements development and HEOR programs

12. The Archimedes Model is used for clinical exploration • Virtual world with simulated people, each with simulated physiological outcomes • Virtual patients based on the profiles of real people • Represented as a series of trajectories of correlated risk factors and clinical biomarkers over a lifetime • Evolving through different health states, accumulating disease burden • Forecasts the clinical outcomes of drug, device, diagnostic, and care interventions by capturing: • Their influence on these underlying trajectories • Secondary and tertiary effects • Resultant changes in risk of disease and clinical events

13. The Archimedes Model is used in HEOR analyses • Used to run virtual clinical trials, registries, and observational studies • Longitudinal insight: Project several years forward • Scenario insight: Test multiple alternate realities • Captures clinical outcomes, utilization, and costs, thereby facilitating economic analyses • The core of the Model is hundreds of algebraic and differential equations ‘translated’ into 150,000 lines of Java code • Continuously validated and updated

14. The scope of the Model is clinical

15. The Model is clinically and administratively detailed, enabling detailed costing analysis • Patient: • Has chest pain and presents to the ER • Receives EKG, chest x-ray, and blood tests • Is diagnosed with MI, admitted to the hospital, and given an angioplasty with drug-eluting stent • Remains in the hospital for 2.1 days and is discharged

16. Several diseases exist in the Model • Diabetes (type 1 and 2) • Diabetes complications • Chronic kidney disease • Coronary artery disease • Atrial fibrillation • Hypertension • Stroke (ischemic and hemorrhagic) • Lung cancer • Breast cancer • Colon cancer • Bladder Cancer • Congestive heart failure • Dyslipidemia • Obesity • Metabolic syndrome • Hypertriglyceridemia • Asthma • COPD

17. Case Study Health Benefits and Cost-Effectiveness of Primary Genetic Screening for Lynch Syndrome

18. The Model was used to answer a critical question in Lynch Syndrome What if we screen individuals for their risk before cancer occurs, and offer genetic tests to those whose risk exceeds a certain threshold ? Situation • Lynch syndrome (LS) is a genetically inherited predisposition to multiple types of cancer including colorectal, endometrial, liver, urinary tract, and others. It is autosomal dominant. • Patients with suspicious family history are referred for genetic consult, but uptake is low • Currently, most testing for LS occurs after an individual develops cancer, at which point the unaffected relatives may also be tested. • Genetic test for LS costs about $3500.

19. Rationale and Approach • Rationale: Identify people at risk at a time when prophylaxis, surveillance, and early detection might be most effective • Study objective was to identify: • Whether primary screening for LS leads to improved health outcomes • Whether such a strategy is cost-effective • An appropriate age to initiate screening by risk assessment • An optimal risk threshold at which to implement genetic testing • Approach was to compare: • 1) experimental arm of at-risk individuals and families using a four-gene panel as screening tool, with • 2) control arm of same individuals receiving standard of care for diagnosis and care

20. Archimedes convened an advisory panel to help guide model-building Archimedes Approach • We recruited a Steering Committee of 5 world-renowned Lynch Syndrome experts to assist in the development of a mathematical model of Lynch Syndrome. • The model was built to include: • A virtual population of 100,000 individuals representative of U.S. population with noncarriers and carriers of several mutations • The development (natural history) of colorectal cancer and endometrial cancer in carriers of Lynch Syndrome mutations • Mutation testing and cancer surveillance/screening • The effects of prevention activities (e.g. colonoscopy) and treatments (e.g., colorectal surgery, hysterectomy, chemo, radiation) on cancer outcomes • The model accounts for a five-generation family history of all LS-related cancers to allow accurate risk prediction based on family history

21. The overall modeling and simulation approach had four stages Build model Validate Simulate Run sensitivities • Models of: • Natural history of disease (CRC, EC) • Mutation prevalence • Family history • Etc. • Other elements: • Test characteristics • Costs • Utility • Surgical/procedure mortality • Compliance (e.g., CRC screening, colonoscopy, endometrial biopsy) Validate against LS registry for # affected relatives, prevalence of 1st degree family history of CRC • Conduct virtual clinical trial with: • 100,000 subjects • 20 primary screening strategies • Current care as a control • Screening strategies based on: • Risk assessment at different ages • 4-gene mutation testing for individuals exceeding different risk thresholds for carrying mutation • Post-test screening of 1st-degree relatives of mutation carriers Examine effect of variations in key assumptions and metrics (e.g., cancer incidence, gene test cost, compliance)

22. Twenty age/risk screening strategies were examined Age Threshold 20 25 30 35 40 Risk Threshold* 0.0% 2.5% 5.0% 10.0% Current Care (Control) 20 Screening strategies Screening Strategy #1 Strategy #2 Strategy #3 Strategy #4 Etc. (up to #20) * Risk thresholds represent the probability of carrying a mismatch repair gene mutation above which to initiate genetic testing Schematic Approach

23. Cost Effectiveness Pre-test probability =0% =2.5% =5.0% =10% Screening start age: Black = 20, Red = 25, Yellow = 30, Blue = 35, White = 40 23 Note: For 100,000 patients

24. ACER vs Screening Start Age Pre-test probability =0% =2.5% =5.0% =10% 24 Note: For 100,000 patients

25. Sensitivity analysis of ACER around important model parameters was performed 25

26. Simulation revealed optimal screening strategies Key Results • Universal screening (age 20 start, 0% risk threshold) leads to good clinical results but is expensive with cost/QALY >$400K • An intermediate approach is optimal • Family history–based risk assessment beginning between the ages of 25 and 35 years followed by genetic testing of anyone with a 5% or higher risk of having mutations • Substantial life savings (12-14% reduction in CRC incidence, 8-9% reduction in EC incidence; 1 LY saved) • At average cost-effectiveness ratio of $26,000 per QALY • Cost-effectiveness is comparable to that of other screening measures (e.g., screening for colorectal, cervical, and breast cancer) • Cost-effectiveness is much more sensitive to risk threshold than starting age of screening • The results are published in AACR Cancer Prevention Research. The AACR organized a press conference on Nov 18, 2010 to discuss the findings

27. Reference Dinh, T.A. et al, “Health Benefits and Cost-Effectiveness of Primary Genetic Screening for Lynch Syndrome in the General Population,” Cancer Prev Res: 4(1) January 2011. Supplementary data for this article are available at Cancer Prevention Research Online (http://cancerprevres.aacrjournals.org/)

28. Topics • Development, Commercialization and Simulation Modeling • Overview of Archimedes Model • Case Study: Lynch Syndrome • Closing Thoughts • Q&A • Appendix: • Case Study: DPP Trial Expansion and Extension (ADA, DHHS) • Illustration: ARCHeS desktop simulation tool

29. Archimedes virtual population simulation can help reimbursement discussions Not a simple Markov-type model – captures many relevant variables and is configurable Generates clinical outcomes, utilization, and costs Longitudinal and scenario insight Captures real-world settings (adjustable compliance) Helps find the optimal health economic situation Can supplement and fill gaps in evidence base Overcomes “chicken-egg” scenario with data/insight available today Less expensive and time-consuming than real-world studies

30. Corporate Overview • Healthcare modeling company • HQ in San Francisco • Core technology - Archimedes Model • Mathematical model of human physiology,diseases, interventions, and healthcare systems • Highly detailed • Carefully validated • In development since 1993 • David Eddy MD, PhD • Len Schlessinger PhD • Owned by Kaiser Permanente • Spun out as independent organization 2006

31. Archimedes Clients and Collaborators (Not all can be shown)

32. Recent Highlights • Sep 17: ARCHeS upgrade • The disease and intervention models in the ARCHeS engine (Simulator) have been upgraded and validated against the most current scientific research and includes a new congestive heart failure (CHF) model. • Jul 27: IndiGO API used in HHS app challenge • App developers for the Million Hearts™ Risk Check Challenge can use the IndiGO API. More... • June 6, 2012: IndiGO Receives Best of Care Applications Award • The award was presented at the Health Data Initiative III Forum. More... • May 24, 2012: Major ARCHeS upgrade • As of today ARCHeS users can customize the delivery of care in their clinical trial simulations. More... • May 3, 2012: HHS Contract • We announced that the U.S. Department of Health and Human Services has contracted with us to use ARCHeS in HHS agencies. More... • Mar. 28, 2012: Quintiles Agreement • We announced an agreement with Quintiles where they will incorporate ARCHeS into their existing solutions and our customers with have access to Quintiles expertise. More... • Jan. 19, 2102: IndiGO at Tulsa Health System • MyHealth Access Network, a Beacon Community in Oklahoma, is deploying our IndiGO platform. This is the third deployment of IndiGO in as many months.More... • Dec. 8, 2011: ARCHeS upgrade and Model (validation) reports available • Upgrade of ARCHeS included numerous improvements to the healthcare processes as well as significant enhancements to the physiology model. Processing speed was increased and several intervention enhancements were made available. Model (validation) reports are now available for download • Dec .7, 2011: IndiGO at Colorado Beacon Consortium • We entered into an agreement with the Colorado Beacon Consortium (CBC) for the use of the Individualized Guidelines and Outcomes (IndiGO) platform. More... • Nov. 17, 2011: IndiGO Program Underway at Fairview • We entered into an agreement with Minnesota’s Fairview Health Services for the use of the Individualized Guidelines and Outcomes (IndiGO) platform. More...

33. Model Description and Validation Report available at http://archimedesmodel.com/resource-centerPlease direct questions to:Badri Rengarajan, MDMedical DirectorBadri.Rengarajan@archimedesmodel.com

34. Appendix

35. The Archimedes Model The Archimedes Model is a mathematical population simulation model of physiology and diseases, interventions, patient/provider behaviors, and healthcare systems

36. Selected Publications Cardiovascular outcomes associated with a new once-weekly GLP-1 receptor agonist vs. traditional therapies for type 2 diabetes: a simulation analysis[ »Diabetes, Obesity, and Metabolism  9/6/2011] Estimating Health and Economic Benefits from Using Prescription Omega-3 Fatty Acids in Patients with Severe Hypertriglyceridemia.[ »Am J Cardiol. 9/1/2011] Individualized Guidelines: The Potential for Increasing Quality and Reducing Costs.[ »Annals of Internal Medicine, 5/2/2011 ] Cost-effectiveness of chemoprevention of breast cancer using tamoxifen in a postmenopausal US population[ »CANCER, 3/14/2011 ] Health Benefits and Cost-Effectiveness of Primary Genetic Screening for Lynch Syndrome in the General Population.[ »Cancer Prevention Research, 11/18/2010 ] Modeling the effects of omalizumab over 5 years among patients with moderate-to-severe persistent allergic asthma. [ »Current Medical Research and Opinion, 11/04/2010 ] Cost-effectiveness of adding information about common risk alleles to current decision models for breast cancer chemoprevention.[ »Journal of Clinical Oncology, 6/07/2010 ] Age at Initiation and Frequency of Screening to Detect Type 2 Diabetes: A Cost-Effectiveness Analysis [ »The Lancet, 4/30/2010 ] [ »View Technical Appendix ] Model-Based Benefit-Risk Assessment: Can Archimedes Help? [ »Clinical Pharmacology & Therapeutics, 12/15/2009 ] Effect of Smoking Cessation Advice on Cardiovascular Disease[ »American Journal of Medical Quality, 5/01/2009 ] The Relationship between Insulin Resistance and Related Metabolic Variables to Coronary Artery Disease: A Mathematical Analysis[ »Diabetes Care Publish Ahead of Print, 11/18/2008 ] A Physiology-Based Mathematical Model of Coronary Heart Disease Accurately Predicts CHD Event Rates in Real Populations[ »Circulation, 11/08/2008 ] The potential effects of HEDIS performance measures on the quality of care[ »Health Affairs, 9/15/2008 ] The Impact of Prevention on Reducing the Burden of Cardiovascular Disease[ »Circulation, 7/29/2008 ] Validation of Prediction of Diabetes by Archimedes and Comparison with Other Predicting Models.[ »Diabetes Care, 5/28/2008 ] The Metabolic Syndrome and Cardiovascular Risk: Implications for Clinical Practice.[ »International Journal of Obesity, 5/1/2008 ] Diabetes Risk Calculator: A Simple Tool for Detecting Undiagnosed Diabetes and Prediabetes.[ »Diabetes Care, 5/1/2008 ] Cure, Care, and Commitment: What Can We Look Forward To?[ »Diabetes Care, 4/15/2008 ] Reflections on science, judgment, and value in evidence-based decision making: a conversation with David Eddy[ »Health Affairs, 6/19/2007 ] Medical Decision-making: Why it must, and how it can, be improved[ »Expert Voices, 5/15/2007 ] Archimedes: A Bold Step Into The Future [ »Health Affairs, 1/26/2007 ] Linking Electronic Medical Records To Large-Scale Simulation Models: Can We Put Rapid Learning On Turbo?[ »Health Affairs, 1/26/2007 ] Accuracy versus transparency in Pharmacoeconomicmodelling: finding the right balance.[ »Pharmacoeconomics, 6/6/2006 ] Bringing health economic modeling to the 21st century.[ »Value in Health, 5/30/2006 ] Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes.[ »Annals of Internal Medicine, 8/16/2005 ] Earlier intervention in type 2 diabetes: The case for achieving early.[ »International Journal of Clinical Practice, 11/28/2005 ] Evidence-based medicine: a unified approach.[ »Health Affairs, 02/15/2005 ] Validation of the Archimedes diabetes model.[ »Diabetes Care, 11/15/2003 ] Archimedes: a trial-validated model of diabetes.[ »Diabetes Care, 11/15/2003 ] Archimedes: a new model for simulating health care systems - the mathematical formulation. [ »Journal of Biomedical Informatics, 02/06/2002

37. Representative Projects Clinical Development Registration Commercial / Launch Clinical Care Payor / Managed Care Case (Coverage, Reimbursement) • Estimating baseline rate for CV events with different DM therapies • Prioritizing phase 1 portfolio • Forecasting long-term benefits of DM renal drug • Simulating head-to-head trial • Analyzing biomarkers and imaging tests for cardiovascular disease screening • FDA research collaboration – simulating risk and benefits of weight loss drug sibutramine • Developing physiology and healthcare system model for Lynch Syndrome • Pricing for gene-based cancer diagnostic • Building health economics case for higher-priced cancer drug • Forecasting benefits of Xolair in decreasing asthma symptoms, exacerbations, and hospitalizations over 5 years • Analyzing cost of obesity for national payor • Analyzing prevention and screening programs in DM, CVD, cancer • Evaluating multiple cancer screening modalities in CRC • Analyzing cost effectiveness of genetic screening tool in breast cancer • Building case for superiority of drug regimen change in staff model HMO care guidelines • Assessing cost effectiveness of several health interventions

38. People Randomized Controlled Trials Real Outcomes Treatments Same? Same (Virtual) People Virtual Outcomes Same (Virtual) Treatments The validation approach is rigorous

39. Over 50 trials have been used to validate the Model 39

40. There are many reasons to consider using simulation in clinical research Large trial population required Several years before data readout Ethical use precludes high-dose or placebo arm Unknown size and profile of eligible population Need for a preview of trial outcomes Need for effectively testing impact of variations in trial design/protocol elements Budget constraints

41. Simulation modeling can help development and commercialization Timely Results Baseline/control arm event rates Eligible population size and composition Preview of trial outcomes Real-world settings CV outcomes and safety studies

42. The Model consists of multiple interconnected physiology modules Example: Type II Diabetes Model Disease progression function FPG HbA1C hits 1 when FPG = 126mg/dL E(df2) df2 Prob(T2DM) FPG0 FPG for non-diabetic Fitted to NHANES BMI Gender/Race Age Family History

43. Case Study DPP Trial Expansion and Extension

44. Only simulation modeling could have enabled expansion and extension of the DPP trial Situation American Diabetes Association (ADA) sought to understand cost-effectiveness of screening and management guidelines to prevent/delay development of T2DM in high-risk individuals Three-year DPP (Diabetes Prevention Program) trial comparing current care, metformin, and lifestyle modification was nearly complete However, ADA and Department of Health and Human Services (HHS) were interested in long-term health and economic outcomes of different strategies, as well as several questions outside scope of DPP trial Existing trial had already cost many $millions

45. The approach involved matching populations & protocols, adding arm, and extending duration Approach Created a simulated population matching DPP inclusion/exclusion criteria and patient baseline characteristics Conducted prospective simulation of DPP trial (same duration, interventions) to validate Model’s ability to reproduce population, interventions, and outcomes Added intervention arm (lifestyle intervention initiated after diagnosis – FPG >125) Extended duration of simulated trial to 30 years

46. The simulation was prospectively validated against the original trial DPP: Diabetes Progression 0.5 0.45 0.4 control 0.35 0.3 metformin Cumulative Incidence of Diabetes 0.25 0.2 lifestyle 0.15 0.1 0.05 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Time (years)

47. Prevent 11% Postpone one decade In simulation, the DPP trial was simultaneously expanded (fourth arm) and extended (30 yrs) 47

48. 22% decrease Estimating longitudinal health outcomes generated comparative effectiveness insight (Baseline vs Lifestyle) 48

49. Estimating longitudinal cost outcomes also generated comparative effectiveness insight (Baseline vs. Lifestyle) 49 49

50. $201,800 $62,600 $24,523 $35,523 The cost effectiveness of each arm over 30 years was revealed 50 50