Health Economics of Stratified Medicines An Industry Perspective

Transcription

1 Health Economics of Stratified Medicines An Industry Perspective Gavin Lewis, Global Head of Oncology Market Access, AstraZeneca Health Economics of Stratified Medicine, London 5 th October 2016

2 Health Economics of Stratified Medicine Key implications, challenges and opportunities Evidence Generation Regulatory and HTA evolution Flexible Pricing 2

3 What is personalised healthcare? AZ defines personalised healthcare (PHC) as the combination of drug and diagnostic test to improve patient outcomes + = 3 Our medicines are most effective when matched to patients individual characteristics Other terms are used for this approach: Personalised medicine Precision medicine Tailored therapy Stratified medicine Diagnostic tests can identify patients characteristics, e.g. What genes drive patients cancer Cell types that predict response in asthma This approach leads to better patient outcomes Targeting medicines to patients who will benefit most Minimising trial and error treatment

4 The evolution of genomics in lung cancer diagnosis Historical View Adeno-carinoma Squamous Large-cell Unknown KRAS Unknown KRAS EGFR Unknown KRAS RET EGFR ROS1 PIK3CA Worldwide, lung cancer is the most common cause of cancerrelated death (1.3M deaths). Traditional classification used morphology Discovery showed that NSCLC cells can harbor a single specific mutated KRAS oncogene AstraZeneca in collaboration with external groups show that clinical response to gefitinib correlates with EGFR mutations Global genomics initiatives (e.g., TCGA) identify multiple additional primary genetic drivers

5 >80% of our clinical pipeline has a PHC approach Phase I Phase II Phase III Applications Under Review 32 New Molecular Entities 26 New Molecular Entities 10 New Molecular Entities 3 New Molecular Entities Small molecule Small molecule Small molecule Large molecule Large molecule Large molecule Small molecule Large molecule AZD1419# TLR9 asthma MEDI0700# BAFF/B7RP1 SLE abediterol LABA asthma/copd AZD9412# Inhaled βifn asthma/copd PT010 LABA/LAMA/ICS COPD anifrolumab# TULIP IFNαR SLE ZS-9 potassium binder hyperkalaemia brodalumab# IL-17R psoriasis AZD5634 inhaled ENaC cystic fibrosis MEDI4920 CD40L-Tn3 pss AZD7594 Inhaled SGRM asthma inebilizumab# CD19 neuromyelitis optica roxadustat# HIFPH anaemia CKD/ESRD benralizumab# IL-5R severe asthma cediranib ICON 6 VEGF PSR ovarian AZD7986 DPP1 COPD MEDI5872# B7RP1 SLE AZD7624 Inhaled p38 inhibitor COPD mavrilimumab# GM-CSFR rheumatoid arthritis acalabrutinib# BTK B-cell blood cancers tralokinumab IL-13 severe asthma AZD8871 MABA COPD MEDI9314 IL4R atopic dermatitis verinurad URAT-1 hyperuricemia/gout MEDI2070# IL-23 Crohns selumetinib# SELECT-1 MEK 2L KRAS+ NSCLC durvalumab# HAWK PD-L1 2L SCCHN AZD9567 SGRM RA MEDI0382 GLP-1/glucagon diabetes/obesity AZD3759 or Tagrisso BLOOM EGFR NSCLC brain mets tezepelumab# TSLP asthma/atopic dermatitis AZD3293# AMARANTH BACE Early Alzheimer's disease moxetumomab pasudotox# PLAIT CD22 HCL AZD4076 mir103/107 NASH MEDI8111 Rh-Factor II trauma/bleeding AZD4547 FGFR solid tumours MEDI4166 PCSK9/GLP-1 diabetes/cv AZD5718 FLAP CAD MEDI0562# hox40 solid tumours AZD5363# AKT breast cancer MEDI6012 LCAT ACS AZD0156 ATM solid tumours MEDI0680 PD-1 solid tumours savolitinib# MET prcc inebilizumab# CD19 DLBCL AZD1775# Wee1 solid tumours MEDI1873 GITR solid tumours vistusertib (AZD2014) mtor 1/2 solid tumours MEDI-573# IGF metastatic breast cancer AZD2811# Aurora solid tumours MEDI3617# ANG-2 solid tumours ATM AVI# BL/BLI SBI MEDI3902 Psl/PcrV pseudomonas AZD4635 A2aR inhibitor solid tumours MEDI4276 HER2 solid tumours CXL# BLI/cephalosporin MRSA MEDI4893 staph alpha toxin SSI AZD6738 ATR solid tumours AZD8186 PI3Kβ solid tumours AZD9150# STAT3 haems & solids MEDI-565# CEA BITE GI tumours MEDI9197# TLR 7/8 solid tumours MEDI9447 CD73 solid tumours AZD3241 MPO Multiple System Atrophy MEDI7510 sf+gla-se RSV prevention MEDI8852 influenza A treatment MEDI8897# RSV passive prophylaxis Oncology RIA CVMD Infection, Neuroscience, Gastrointestinal Project with PHC Approach AZD9496 SERD ER+ breast MEDI1814 amyloidβ Alzheimer's disease AZD8108 NMDA suicidal ideation MEDI7352 NGF/TNF osteoarthritis pain Pipeline data correct as of 28 July Includes significant fixed-dose combination projects, and parallel indications that are in a separate therapy area (See LCM chart for other parallel indications and oncology combination projects) # Partnered and/or in collaboration; Registrational P2/3 study

6 PHC: Delivering better, safer and more efficacious treatments Conceptually PHC aligns with the goals of Payers Personalized HealthCare To deliver better, safer, more effective treatments To better understand disease diversity or subtypes To identify the differences between patients To identify the best drug targets To improve the quality and efficiency of R&D Health Technology Assessment Better and more predictable clinical outcomes Improved quantity and quality of life Fewer unnecessary treatments / side effects and associated costs Better compliance Optimized use of resources in healthcare 6

7 Evidence Generation Adaptive pathways can be defined as a prospectively planned, iterative approach to bringing medicines to market. The iterative development plan will initially target the development to a well-defined group of patients that is likely to benefit most from the treatment. This is followed by iterative phases of evidence gathering and progressive licensing adaptations, concerning both the authorised indication and the potential further therapeutic uses of the medicine, to expand its use to a wider patient population as more data become available A What challenges arise when shifting from A to B? How should HTA evolve to accommodate earlier license approval and assessment? B 7

8 Evidence Generation and HTA methods Health Economics has tools to support evolving regulatory pathways Indirect Comparison methods Extrapolation and modelling Single Arm Studies Surrogate Endpoints Data Maturity Multiple Indications / Combos Agile reassessment Risk share arrangements Flexible pricing RWE sources 8

9 Large variation in benefit assessments by HTA institutions with opportunity for greater harmonisation and development signals to industry 9

10 Payers may recommend more restrictive diagnostic cut-off on grounds of cost effectiveness What criteria for cut-off? Not always binary e.g. level of PDL1 expression Ex-ante versus ex-post confirmation of eligible population and price impact Cost-effective below the red line 0 All comers Medium / high High Overall survival Overall cost Cost-effectiveness

11 Multiple indication oncology medicines increasing dramatically with personalised medicine

12 Price should correlate to value to enable patient access and drive correct development incentives* Value based price to enable access 150 Should combo price reductions apply to all previous and future indications? Drug A Drug B Combinaiton VBP Combo New Drug A New Drug B 12 *Numbers are purely for illustration only and not representative of expected prices

13 Conclusions Access to stratified medicines can be improved by Greater integration of clinical evidence requirements between regulators and payers when evaluating benefit of personalised medicines 2. Increased adoption of evidence synthesis, disease modelling and management of uncertainty methods by HTA institutions 3. Healthcare systems ability to implement pricing flexibility to align price and value as evidence evolves and new indications emerge 4. Investment in data sources to track medicine utilisation, outcomes and implement patient access schemes 13