Health Economics of Stratified Medicines An Industry Perspective

Gavin Lewis, Global Head of Oncology Market Access, AstraZeneca Health Economics of Stratified Medicine, London 5th October 2016

Health Economics of Stratified Medicine Key implications, challenges and opportunities

2

Evidence Generation

Regulatory and HTA evolution

Flexible Pricing

What is personalised healthcare?

• Diagnostic tests can

identify patients’ characteristics,

e.g.

• What genes drive patients’

cancer

• Cell types that predict

response in asthma

• Our medicines are most effective

when matched to patients’

individual

characteristics

• This approach leads to better

patient outcomes • Targeting medicines to

patients who will

benefit most

• Minimising trial and error treatment

3

Other terms are used for this approach:

• Personalised medicine

• Precision medicine

• Tailored therapy

• Stratified medicine

+ =

AZ defines personalised healthcare (PHC) as the combination

of drug and diagnostic test to improve patient outcomes

Unknown EGFR

KRAS ROS1

RET PIK3CA

2012 2004 1987 Historical View

Worldwide, lung cancer is the

most common cause of cancer-

related 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”

Adeno-carinoma

Squamous

Large-cell

Unknown KRAS EGFR Unknown KRAS

The evolution of genomics in lung cancer diagnosis

>80% of our clinical pipeline has a PHC approach

Pipeline data correct as of 28 July 2016

1 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

Phase I Phase II Phase III32 New Molecular Entities 26 New Molecular Entities 10 New Molecular Entities

Small molecule Small molecule Small moleculeLarge molecule Large molecule Large molecule

Applications Under Review3 New Molecular Entities

Large moleculeSmall molecule

AZD1419#TLR9 asthma

AZD5634inhaled ENaC cystic f ibrosis

AZD7986DPP1 COPD

AZD8871MABA COPD

AZD9567SGRM RA

MEDI0700#BAFF/B7RP1 SLE

MEDI4920CD40L-Tn3 pSS

MEDI5872#B7RP1 SLE

MEDI9314IL4R atopic dermatitis

abediterolLABA asthma/COPD

AZD7594Inhaled SGRM asthma

AZD7624Inhaled p38 inhibitor COPD

AZD9412#Inhaled βIFN asthma/COPD

inebilizumab#CD19 neuromyelitis optica

mavrilimumab#GM-CSFR rheumatoid arthritis

MEDI2070#IL-23 Crohns

tezepelumab#TSLP asthma/atopic dermatitis

verinuradURAT-1 hyperuricemia/gout

anifrolumab# TULIPIFNαR SLE

benralizumab#IL-5R severe asthma

PT010LABA/LAMA/ICS COPD

tralokinumab IL-13 severe asthma

brodalumab#IL-17R psoriasis

AZD4076miR103/107 NASH

AZD5718FLAP CAD

MEDI0382GLP-1/glucagon diabetes/obesity

MEDI8111Rh-Factor II trauma/bleeding

MEDI4166PCSK9/GLP-1 diabetes/CV

MEDI6012LCAT ACS

roxadustat# HIFPH anaemia CKD/ESRD

ZS-9potassium binder hyperkalaemia

AZD0156ATM solid tumours

AZD1775#Wee1 solid tumours

AZD2811#Aurora solid tumours

AZD4635A2aR inhibitor solid tumours

AZD6738ATR solid tumours

AZD8186PI3Kβ solid tumours

AZD9150#STAT3 haems & solids

AZD9496SERD ER+ breast

MEDI0562#hOX40 solid tumours

MEDI0680PD-1 solid tumours

MEDI1873GITR solid tumours

MEDI3617#ANG-2 solid tumours

MEDI4276HER2 solid tumours

MEDI-565#CEA BITE GI tumours

MEDI9197#TLR 7/8 solid tumours

MEDI9447CD73 solid tumours

AZD3759 or Tagrisso BLOOMEGFR NSCLC brain mets

AZD4547FGFR solid tumours

AZD5363#AKT breast cancer

inebilizumab#CD19 DLBCL

MEDI-573#IGF metastatic breast cancer

savolitinib#MET pRCC

vistusertib (AZD2014)mTOR 1/2 solid tumours

acalabrutinib#BTK B-cell blood cancers

durvalumab# HAWK¶PD-L1 2L SCCHN

moxetumomab pasudotox# PLAITCD22 HCL

selumetinib# SELECT-1MEK 2L KRAS+ NSCLC

cediranib ICON 6VEGF PSR ovarian

ATM AVI#BL/BLI SBI

CXL#BLI/cephalosporin MRSA

MEDI3902¶Psl/PcrV pseudomonas

MEDI4893staph alpha toxin SSI

MEDI7510sF+GLA-SE RSV prevention

MEDI8852inf luenza A treatment

MEDI8897#RSV passive prophylaxis

AZD8108NMDA suicidal ideation

MEDI1814amyloidβ Alzheimer's disease

MEDI7352NGF/TNF osteoarthritis pain

AZD3241MPO Multiple System Atrophy

AZD3293# AMARANTHBACE Early Alzheimer's disease

Oncology RIA CVMD Infection, Neuroscience,

Gastrointestinal Project with PHC Approach

Conceptually PHC aligns with the goals of Payers

PHC: Delivering better, safer and more efficacious treatments

• 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

• 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

Personalized HealthCare Health Technology Assessment

6

Evidence Generation

7

• 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

• What challenges arise when shifting from A to B?

• How should HTA evolve to accommodate earlier

license approval and assessment?

B

A

Evidence Generation and HTA methods Health Economics has tools to support evolving regulatory pathways

8

• Flexible pricing

• RWE sources

• Agile re-assessment

• Risk share arrangements

• Extrapolation and modelling

• Indirect Comparison methods

Single Arm Studies

Surrogate Endpoints

Multiple Indications / Combos

Data Maturity

Large variation in benefit assessments by HTA

institutions with opportunity for greater harmonisation

and development signals to industry

9

Payers may recommend more restrictive diagnostic

cut-off on grounds of cost effectiveness

0

5

10

15

20

25

30

35

All comers Medium / high High

Overall survival Overall cost Cost-effectiveness

Cost-effective

below the red line

• 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

Multiple indication oncology medicines increasing

dramatically with personalised medicine

0

50

100

150

200

250

Drug A Drug B Combinaiton VBP Combo New Drug A New Drug B

Value based price

to

enable access

Price should correlate to value to enable patient access

and drive correct development incentives*

12 *Numbers are purely for illustration only and not representative of expected prices

Should combo price

reductions apply to all

previous and future

indications?

Conclusions Access to stratified medicines can be improved by...

13

1. 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