How Personalized Healthcare May Bring New Life to Failed Drugs

Transcription

1 Session 5: Niche brokering: finding new users for old knowledge How Personalized Healthcare May Bring New Life to Failed Drugs OECD Expert Workshop on Knowledge Markets in Life Sciences National Academies of Science Washington, D.C October 2008 Slide 1

2 Personalized Healthcare Generating meaningful segmentation of patient populations, by whatever technology is appropriate (genomic, imaging, informatic), in order to increase the benefit of therapy 2

3 Everybody responds to therapy differently Percentage Non-responders Who suffers when therapies don t work? Patients Physicians Payers 3

4 Targeted Clinical Trials Estimated that drug companies could save up to $100 sm per drug by incorporating pharmacogenomics data into trials Trials terminated early due to success in a genetically defined subset of breast cancer patients Interim analysis yielded statistically significant results, exceeding the primary endpoint 4

5 Access to the right therapy Patient Population Severe Symptoms = Predicted Responders = Predicted Low Efficacy or Side Effects Moderate Symptoms Mild Symptoms 5

6 $m Quicker uptake of therapeutic value Breast Cancer Arimidex Therapies: vs. Herceptin: Global Sales from launch Launch 3,500 3,000 2,500 Traditional PHC Adjuvant approval EMEA May 06, US FDA Nov 06 2,000 1,500 ASCO presentation of adjuvant data (May T 05) 5 year analysis of ATAC data 1, Initial adjuvant indication in US

7 Towards Preventive Medicine Avoiding futile medicine Predictable therapeutic response Earlier intervention Delay onset and minimize severity 7

8 Rescue Strategies The Good: Herceptin/Herceptest The Bad: Herceptin/Herceptest The Ugly: Herceptin/Herceptest The Really Ugly: Iressa 8

9 $m The Good - Herceptin Large responder effect in pivotal trials FDA will not approve without Dx Genentech develops IHC (Immunohistochemistry) with Dako Receives approval and launches Herceptin/Herceptest First blockbuster Personalized Therapy Breast Arimidex Cancer vs. Therapies: Herceptin: Global Sales Sales from launch from Launch 3,500 3,000 2,500 2,000 Traditional PHC Adjuvant approval EMEA May 06, US FDA Nov 06 ASCO presentation of adjuvant data (May 05) 5 year analysis of ATAC data 1,500 T 1, Initial adjuvant indication in US

10 The Bad - Herceptin Genentech develops second test based on FISH (fluorescence in situ hybridization) with Vysis (now Abbott Molecular) But: According to guidelines released by ASCO and the College of American Pathologists, analyses of prospective, randomized, adjuvant trials of Herceptin show that testing algorithms to gauge HER2 expression have not been standardized and were developed somewhat arbitrarily. An expert panel found as many as 15 percent to 20 percent of the HER2 assays performed in the field may be incorrect when the same specimen was reevaluated in a high-volume, central laboratory. Current testing methods for determination of the likelihood of benefit from Herceptin are not adequate," Third test, CISH (Chromogenic In Situ Hybridization), now available from Invitrogen New test in the works from Monogram based on a proximity-based assay 10

11 The Ugly - Herceptin Herceptin and HER-2 tests are reimbursed separately Use of the HER-2 tests is not required by the label Poor reliability and reduced trust in the HER-2 tests results in poor use of the tests by oncologists Adverse (ultimately overturned) costeffectiveness decision by NICE on use of Herceptin without HER-2 testing 11

12 The Really Ugly - Iressa Iressa shows high levels of efficacy against non-small cell lung cancer in early trials Very clear responder population in pivotal trials results in very limited label AstraZeneca retracts the drug for all but compassionate use Two academic labs suggest a role for EGFR in predicting drug response for Iressa and Tarceva Iressa becomes the poster child for linked RxDx But biological complexity rears its ugly head Subsequent research indicates EGFR positivity doesn t predict for drug response, that the biology is much more complicated Still no predictive test for Iressa 12

13 The Lesson Herceptin/Iressa Assuming the biology works: No standards for sensitivity/specificity of Dx tests linked to therapeutics Coverage/reimbursement decisions by public payers for new generation Dx, much less RxDx, antiquated and don t provide ROI for clinical utility Private payers want demonstration of clinical effectiveness FDA indicating it will regulate this space Business model for new generation Dx may be untenable Could the Herceptin story replay today? Possibly Could the Herceptin story replay tomorrow? Probably not Retrofits of a Dx to a developed Rx are hard, start early 13

14 The PHC Landscape Today PM has broad support but will require changes in how personalized technologies are evaluated, how health care is financed and delivered, and how clinicians and consumers are prepared Policy-makers, researchers and developers of PM interventions need to develop strategies to overcome barriers to clinical integration Stakeholders require extensive and on-going education in PM, in particularly the payer/purchaser community and health care delivery organizations/providers Multi-stakeholder collaborations, including public-private partnerships, will be required Source: Duke/ABT/PMC Landscape Analysis 14

15 Future Issues for PHC Evidence requirements and reformed reimbursement policies reflecting PM product value Intelligent legislation/ regulation Enabling technologies, such as HIT PM-savvy health care workforce Source: PMC Strategic Plan Viable economic/ business models for PM offerings 15

16 Repurposing Old Knowledge Informatic PHC Can a large and fully integrated Electronic Health Record System (EHR) be used to demonstrate the value of antidiabetic therapy, in terms of comparative benefit and risk, in an environment reflecting actual clinical use of the therapy? 16

17 Cohort >18 years old Diagnosis of Diabetes in EHR. Prescribed single oral hypoglycemic Agent. Patients with the metabolic syndrome and polycystic ovarian syndrome excluded Patients on AGI excluded (n=146) Baseline = Earliest Date of Hypoglycemic RX in a diabetic patient. Pts without follow-up were censored for all outcomes except mortality. 17

18 Predictors Measurements: Lipids, BMI, BP, LVEF, Hba1c, egfr, LFTs, Alb:Cr Medical History: CHF, Liver Disease, Kidney Disease, CAD, Stroke/TIA, Hepatitis B/C, Atrial Fibrillation, New Diabetic? Demographics: Age, Gender, Race Oral Hypoglycemic Medication Class: Meds: ACE / ARB, Cholesterol, ASA, Plavix 18

19 Medication Classes Abbreviation Class Examples Big TZD SFU Dphen & Meg AGI biguanides thiazolidinediones sulfonylureas D-phenylalanine derivatives. AKA. Meglitinides Alpha-glucosidase Inhibitors Glucophage (metformin) Avandia (rosiglitazone) Glucotrol (glipizide) Starlix (nateglinide) Precose (acarbose) 19

20 Outcomes Nephropathy Renal Insufficiency Mortality Coronary Artery Disease Stroke/TIA Liver Injury Heart Failure Continuous Outcomes: BMI, Hba1c, LDL, HDL, TG. 20

21 Creating the Data Set CCF has utilized an EHR since EHR Tethered PHR eprescribing Large Clinical Data Repository 3.5 million patients 5 million prescriptions 89 million laboratory results Scope of Data Medications Demographics Diagnoses Procedures Laboratory Imaging Clinical Data Repository Social Security Death Index CCF DM Data Set CCF DM Definitions Anil Jain, MD Copyright 2007, Cleveland Clinic

22 Conclusions Successful use of the EHR to predict outcomes in type II diabetes Models are very good at predicting clinical outcomes Models for predicting continuous outcomes are better than simply relying on baseline alone. Interactions allow for tailored treatment beyond AHRQ recommendations. Potential to extend these techniques to other diseases. 22

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24 Repurposing Old Knowledge for: Better understanding of the structure and interactions in Clinical and Translational Science STEM-01: Genetic medicine STEM-02: Female reproductive cell biology STEM-03: Vascular disease & control of cell proliferation Stem Cell Science STEM-05: Gene and cell therapy STEM-04: Controls in early development STEM-10: Diabetic cell therapy STEM-06: Stem cell therapy STEM-08: Cell transplantation & Tissue regeneration STEM-07: Control of hematopoiesis STEM-09: Genetic control of cell differentiation STEM-12: Signaling pathways in cell differentiation STEM-14: Neural regeneration STEM-13: Early development and organ differentiation STEM-11: Molecular biology of cell differentiation 24

25 Repurposing Old Knowledge for: Distinctive Competencies for Clinical and Translational Science -omic understanding of disease 25

26 Thank-you Wayne A. Rosenkrans, Jr., Ph.D. Distinguished Fellow, MIT Center for Biomedical Innovation Chairman & President, Personalized Medicine Coalition Chief Applications Officer, SciTech Strategies Slide 26