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Embracing Innovation

to avoid a Productivity Plateau

NY Pharma Forum | September 21, 2016

CONFIDENTIAL AND PROPRIETARY

Any use of this material without specific permission of McKinsey & Company is strictly prohibited

McKinsey & Company 1McKinsey & Company 1

Summary

Pharma is attractive . . .

and will remain so

Innovation is increasing the diversity of opportunities

to address industry productivity challenge

R&D models and resources need to be

adapted to take advantage of the diversity

McKinsey & Company 2

25 years of FDA NMEs

SOURCE: FDA; Nature Reviews Drug Discovery; Web search; EvaluatePharma; McKinsey

50

30

0

40

10

20

60

20142000 121002 0804 069694 98921990

753approved

drugs 134biologics

192orphans 12

drugs withCDx


41

27

3930

212524182220

362723

Slow and steady pivot to specialty

35%48%

65%52%

20142002 2011201020092006 20122008200720052004 20132003

23

SOURCE: FDA; web search; EvaluatePharma; McKinsey

Specialty2

Primary%, cumulative

FDA drug

approvals (#)

7 179131167865796Orphan (#)

FDA drug

approvals

by TA#, cumulative

Blood & Oncology

Other3

CNS

Anti-infectives

Endo & MSK

GI & GU

CV

98

75

45

44

34

34

5 1182010101210653107Specialty1 (#)

1 Non-orphan specialty drugs 2 Includes specialty and orphan drugs 3 Includes Dermatology, Respiratory, Sensory Organs, and Various


Sustained improvement in standard of care

5

10

3

86 5 6 7 6

14

7

4 53 3

6

9

5 6

9

22

19971996

Below 50%

improvement

20032000 2002 2005

Over 50%

improvement

20062004200119991998Launch

year

50%

efficacy

improve-

ment

SOURCE: McKinsey Drug analog database; FDA Online Label repository; Carnegie Mellon University Center for Economic Development; Forbes

1 Efficacy data from the label of 351 drugs launched between 1996 and 2006. Drugs with efficacy data derived from trials using placebo as comparator were excluded.

When different doses were used in the clinical trial, efficacy improvement over comparator was averaged across doses

Drugs with efficacy improvement1 above or below 50% over standard of care

Total, #

71

60


Hepatitis C

Big efficacy wins in targeted populations but “tailoring of medicines for all” remains a dream

1 Scored on PASI 90, comparison of leading clinical candidates with Humira

Response rates Non-responders

Psoriasis1

CLL

2002 2014

53%

53%

55%

12%

12%

8%

SOURCE: Company websites; PubMed; News media

Precision medicine:Tailoring of medical treatment to the

characteristics of an individual patient

(moving above and beyond stratifying

patients into treatment groups based

on phenotypic biomarkers)


Accelerating environmental changes

Headwinds Tailwinds Uncertainties

Increased

demand for

evidenceEconomic

downturns

Aggressive payor

pressure

Intense industry

competition

Growth of global

generics and

biosimilars

Favorable

demographics

Improving science

/ technology

infrastructure

Increased

investment and

substrate

Disruption by

new players


Incremental impact on median IRR

1 Portfolio includes annual throughput of 4 assets (1 PhI oncology, 1 PhII oncology, 1 PhI autoimmune, 1 PhII rare disease) under a partnership model

2 Upside case is 2X cumulative PTS 3 Upside case skews the revenue distribution 4 Upside case increases royalties by 5-10%

5 Upside case reduces cost by 20% across phases 6 Upside case increases milestones by 15% of dev cost for successful advancement

7 Upside case decreases time by 20% across phases

Base case IRR distribution1

80

40

30

20

10

07050-10 403010 600 20

100

60

70

90

80

50

IRRPercentage

Cumulative probabilityPercentage

90th percentile

Median

10th percentile

+1.1

+0.9

-1.0

-3.1

-2.2

+2.2

+2.4-4.6

+9.7-8.5

+11.5-9.2PTS2

Time7

Revenue3

Royalty rate4

Downside

Upside

Select TAs and portfolio mixes remain highly attractive

Percentage points

Milestone

payments6

Cost5

Illustrative

Median revenue

(first 10 years)

Median investment

(first 5 years)

Median IRR %%

19

19

$1.42B

$1.01B

Portfolio of oncology, autoimmune, and rare disease assets delivers IRR of ~20%


Overall, a downward pressure on value

SOURCE: EvaluatePharma 2014; McKinsey

150

50

500

400

450

350

200

250

100

0

300

2012

1999-2003

1994-1998

2009-2014

Before 1988

1989-1993

2004-2008

2020E1994 20041986

Pharmaceutical revenues from NME-grade

products1

$ Billions

1 Excluding generics, biosimilars and OTC, NDA and new derivatives; Includes all NME-grade innovative products (also new biologics, vaccines and blood products as per CBER BLA designation)

2 Estimated seven year annual sales (actual or forecasted) for visible2 compounds: only products with revenue and launch date forecasts available

First market

introduction

Median revenues from NME-

grade products2

$ Billions, 3-year rolling average

1998 2000 2002 2004 2006 2008 2010

3.0

5.5

3.5

4.5

2.5

2.0

4.0

5.0

1.0

0

0.5

1.5


0.119751970

10.0

201520101980 1990 1995

1.0

20001985 2005

100.0

R&D productivity continues to decline? Plateau?. . .

SOURCE: NME data for 1970-71 from Peltzman, S. (1973) Journal of Political Economy Vol. 81; NME data for 1972-79 as reported in Hutt, P.B. (1982) Health Affairs Vol. 1; NME data for 1980-1996 from Parexel’s Pharma R&D Statistical Sourcebook; NME data for 1996-2013 from Mullard, A. (2014) NRDD Vol. 13; for 2014 as per FDA data; industry R&D spend data from PhRMA Annual Membership Surveys; Kaitin et al., New drugs of 1987-1989 J Clin Pharm(1991) p116; Frantz et al., Nature Reviews Drug Discovery, (2003), p 95; Kaitin et al, New Drugs of 1993-1995, American Journal of Therapeutics (1997), p46

1 Includes NMEs and BLAs. BLAs included 1986 onward; biologics approvals in prior years assumed negligible 2 Restricted to PhRMA member companies

New drugs approved1 1970-2014Per $ Billions of R&D spend2

“Global Pharmaceuticals: R&D Productivity Finally

Turning the Corner?! Important New Data

Suggests It Is”

“These data suggest that the much heralded record number of NCE approvals did not indicate a trend toward a greater number of

annual drug approvals”

“The data presented offer encouraging evidence of faster NDA approval times, and rapid access to drugs intended to treat life

threatening diseases”

“Looking at the gradual slide in numbers over the past few years clearly shows how

the absence of new products emerging from the pipeline – despite more R&D spending than ever – is creating a feeling of unrest

among industry management and analysts


R&D models have revolved around 3 potential approaches

“Pick the winner” “Break the funnel”Traditional

development funnel

Descrip-

tion

▪ Testing many ideas in a few TAs,

forced early attrition of assets

▪ Aggressively exploring diversity

early and cheaply

▪ Willingness to give up potentially

good assets to avoid late-stage

penalties

▪ Pursuing few ideas with

significant clarity around

problem and solution, lower

attrition of assets

▪ Mostly but not exclusively

focused on orphan diseases

▪ Leveraging scale, ‘shots on

goal’, natural attrition of assets

▪ Typically pursuing a few ideas

across multiple TAs

What you

have to

believe

▪ Broad access to diversity,

impossible to “pick the winner”

▪ Average potential commercial

valuation will not offset expensive

late stage failure

▪ Unique scientific insights and

clear markers of success exist

to address a well-defined

medical problem

▪ Limited number of targets and

pathways to pursue

▪ Ample resources allow multiple

bets or increased exploration

after failure

▪ High potential commercial valu-

ations offset development cost


We simulated the performance of the R&D models based on historical and recent data

Questions

What model best takes

advantage of evolving

innovation diversity?

How productive is the traditional

development funnel in

today’s market conditions?

What impacthave select industry strategies (e.g., TA

focus, improved asset quality) had

on competitive productivity advantage?

▪ Realistic simulation of portfolio

evolution

▪ Examining impact of decision-

making and tradeoffs via Monte

Carlo simulation and Bayesian

statistics

▪ Trends and data from the late

1990s and last 5 years, including:

– PTS and decision quality by

phase

– Trial cost and time by phase

– Commercial revenues

Model

Can efficiently exploring diversity

with sharp-decision making (“breaking the

funnel”) enhance productivity?


Traditional development funnel may have worked in the past, but it is no longer viable

Cumulative

launches in

5-year

period at

steady state

Productivity

index112.4

2.0

3.2

2.0

1998 2014

decrease in

productivity4x

Dev

time

Costs

Drivers:

Revenue

1 Productivity index = NPV (revenue) / NPV (development cost), averaged over n=1000 simulations


Some companies succeed through TA focus and improved asset quality, but achieving this is difficult

Traditional

approach

Traditional approach

focused player

“Pick the

winner”

3.2

2014

9.1

Market

leader

Industry

average

4.3

13.610.1

Industry

average

Market

leader

Focus on oncology Focus on diabetes

20.7

Industry

average

Market

leader

21.9

▪ 22% of assets

entering the

pipeline succeed

vs. 11% across

industry

▪ 31% of assets

entering the

pipeline succeed

vs. 14% across

industry

▪ PTS similar

across industry

players and

market leaders

Advantage:

2.1x1.4x

1.1x

Rare diseases

Productivity index1



Increasing decision quality can also have an effect onfocused players

Oncology

Productivity

index1

4.3

10.1

9.1

13.6

10.0

16.1

Industry avg. Market leader

Market leaders

with increased DQ

▪ Market leaders have similar decision making quality as industry players

▪ Increasing the decision quality can have a positive effect on ROI

Diabetes



Testing many ideas with sharp decision making – “break the funnel” – can increase the productivity

1 Modeled as reduction in false-positive rate 2 Modeled as reduction in false-positive rate, increased PTS, and increased throughput

4.73.73.2

increase in

productivity1.5x

4.5

2.02.0

Traditional

model

“Break the

funnel”2

Better decision

quality1

Cumulative

launches in

5-year

period at

steady state

Productivity

index


4236

40

55

45

60

35

30

25

20

50

15

10

0403834

5

3026162 180 286 20128 2214 24104 32

Sweet spot for scale and focus to enhance PTS and portfolio sustainability

Low High

Low

# In

dic

ation

s

# Biologics in Pipeline

High

Bubble size: 2025E biologics revenue

Focused players

Broad players

SOURCE: Pharma Projects, Evaluate

Segmentation of top biopharma pipelines by number of biologics and indications1,%

1 Innovative biologics (excluding biosimilars and anti-infective vaccines) in Ph I – III 2 Pipeline and indications do not include Allergan


Productivity of R&D has declined

3-4x over the last 15-20 years

Players could adopt the “break the funnel” strategy to

efficiently explore diversity and enhance productivity

Historically, a “break the funnel” strategy was not viable

because of market dynamics (high commercial value, low

development cost) and absence of sufficient diversity

Increased decision quality can improve the

productivity of even the most focused market leaders

Summary of takeaways


Assessing R&D innovation performance – a framework

Asset # 3

Asset # 2

Asset # 5

Asset # 4

Asset # 1

Risk

Incentivization

Quality of decision making5

Franchise momentum9

Growth pillars8

Productivity10

Sourcing model1

Innovation aspiration2

Modality diversity3

TA expertise4

Innovation sourcing R&D Engine Performance

Breadth Depth

Project selection and

portfolio balance6

Low

High

Low HighInnovation

Risk

Phase I ($) Phase II ($$) Phase III ($$$)

Early attrition

Traditional funnel

Short(1-3 years)

Medium(3-7 years)

Long-term(7+ years)

TA 1TA 2

TA 1TA 2

TA 3

TA 4TA 3

Focused

TA 1TA 2

TA 3

Broad

TA 4

TA 5

TA 1TA 2

TA 3

Best-in-class

External

Biologic

First-in-class

Internal

Small molecule Functional integration7

Innovation

engine

Competitive

intelligence /

forecasting

Market

access

Medical /

Clinical

Portfolio

management

Marketing


Specialist

call-point

based on sub

population

targeted

Expand into broader

populations or adjacent

indications with shared

symptoms/ mechanisms

Sentinel population

Seek large effect sizes

and signal clarity using

defined sub-populations

and rare disease patients

Focus primarily

on specialists or

“super”specialists

Target Sub-populations

CommercialClinical Development2 3Broad/adjacent populations

Broad

population

Pursue innovative

science and novel

MoAs addressing

challenging

patient needs

Discovery1

Rare

disease

Main indication

Indication expansion 1

Indication expansion 2

Focus

Exp

an

sio

n

Most (not all) current R&D strategies pursue some type of sentinel model


Addressing productivity --- A few observations from the field

Surfing the “S curves”

Back to the “Classics” Searching for “Exo”intelligence

Embracing “Pheno”menology


Critical set of capabilities needed regardless of strategy

Building a dynamic

organizational

design capability to

frequently adjust

and optimize the

organization

without disruption

In-depth market access

and customer insights to create

a product strategy which delivers

cutting edge science while meeting

the demand for value from payors and

other stakeholders

An active BD stance, robust capabilities,

and a clear risk appetite

to source innovation

and tap into diversity

early, swiftly, and

decisively via

creative deal

structures and

multiple bets across

platforms, targets

Early

deal-makingAgility

New product

planning


Significant gaps in generating insights for new product development

Question: What are your biggest pain points for forecasting today?

Percent of respondents

(n = 39)

13

23

26

28

38

41

44

49

51

67

72

Lack of transparency into key assumptions

No standardized tools and methodologies

Lack of systems and technology

Lack of relevant analogs

Fragmented process involving multiple handoffs

Lack of resources / manpower

Lack of ownership and accountability

Poor market insights and competitive intelligence

Lack of expertise / insights into key forecast drivers

Internal bias around our product, capabilities,and the competition

Predicting external events

SOURCE: 2015 Survey of pharma forecasters


Evolving ecosystems require more agile organizations

SOURCE: Interviews; press; Web sites; McKinsey

Adhocracy Agile

Trapped Stalwart

Weak Strong

Stable backbone

We

ak

Str

on

g

Dynam

ic c

apabili

ty

What it is like . . .

Start-up

Chaos

Creative

Frenetic

“Free for all”

Ad hoc

Unpredictable


Uncoordinated

Stuck

Empire building

Fire-fighting

Local tribes

Finger pointing

Rigid


Quick to mobilize

Collaboration

Responsive

Free flow of information

Quick decision-making

Empowered to act

Resilient


Risk averse

Standard ways of working

Silos

Decision escalation

Reliable

Centralized

Established


Many are evolving to an ecosystem-oriented, externalized approach

2015:Today Big Pharma is still inward facing and struggling

with productivity

Platform

service

providers

Physicians /

KOLs

Data /

analytics

providers

Academia

PayersBiotechsBig

pharma

The productivity challenge . . . . . . rebalanced by 2030

2030: Becoming more externally networked in an “innovation

ecosystem” can help crack the productivity challenge

Academia

Biotechs

Platform

service

providers

Data /

analytics

providers

Payers

Physicians /

KOLs

Big pharma

Collocate to facilitate idea

exchange / co-development

Share perspectives

on likely evolution of

clinical pathways

Enable pharma to tap

into new technologies

e.g., in silicoAccelerate target

finding, validation and

lead optimization

Co-develop new

pricing models

Pull in pharma

expertise to scale

up and launch

% of research

spend internally

% launched products

sourced internally

20-30%20-30%

% of research

spend internally

% launched products

sourced internally

80-90%

40%


Incumbents experimenting with data and analytic initiatives across their value chain

SOURCE: Company reports; press releases

NOT EXHAUSTIVE

Centralizing function to build scale (typically within R&D but also relevant to Commercial)

Data partnerships with major health plans, PBMs, regulators, other players in ecosystem

Joint analytic efforts with health plans to determine efficacy and outcomes -the devil you know is better than the one you don't

Specific measures of economic impact for making go/no go decisions on phase 3 and 4

Capability building – tools, talent/health economist & medical informaticists, buy vs. build decisions

Change management programs to educate and convert organization

Post-marketDevelopmentDiscovery

Research

Genomics

Biomarkers

Claims

Multichannel

marketing analytics

Trial operations

Mytras

Pill plus

EMRs

Real-time

tracking

Companion

diagnostics


At the same time, an ecosystem of new players is emerging from prevention to chronic care

1 Many of these technologies are doing more than one of these at once

Type of digital

solutions1

Enhance

connectivity

to remove

need to be

co-located

Utilize “big

data” and

advanced

analytics

Automate

previously

manual tasks

and reduce

variation

3

1

2

Choose the

right careStay healthy Access care

Provide

treatment

Manage chronic

conditions

SOURCE: Company reports; press releases

NOT EXHAUSTIVE

http://www.welldoc.com/

http://www.welldoc.com/


Focusing on phenotypic subpopulations to solve the efficacy

problem - From disease as single entity to subdomains

Schizophrenia

Major depressive disorder

Other psychiatric diseases

Schizophrenia

+veSymp.

-veSymp.

CognitiveImpair-ment

Executive function

Working memory

Delusions Anhedonia

Amotivation

Avolition

Asociality

Thought

disorders

Hallucinations

Attention

Verbal learning

Visual learning

Reasoning Social cognition

Disease

Domain

Sub-domain

ILLUSTRATIVE


100,000

10,000

100

10,000,000

10

1,000

1,000,000

1

Rapidly evolving diagnostic landscape enabling patient “fingerprinting”--- e.g., next generation sequencing (NGS)

SOURCE: Company Web sites; Literature; Molecular Cell; McKinsey

454 GS-20

Pyrosequencer

Oxford

Nanopore

MiniON

Illumina

HiSeq

X Ten

More

compact

Higher

output

Machine output (Mb)

High throughput screening

applications, (% distribution)

20152005 Year

1 Estimated

30x human genome

100x human exome

10

15

35

20

55

7

5

21

14

18

15

10

6

65

Gene expression

Genome regulation

Translation

Replication

2009-

2014

Genome sequencing

4

Transcription

2005-

2008

Genome organization

RNA biology

Other

20 72 Total applications

~11,500

NGS install

base 2017E


Chimeric Antigen

Receptor T cell

therapy (CAR-T)

1 E.g., Nanotechnologies, bioelectronics, virus particles 2 Currently ~60% of global clinical pipeline

SOURCE: Pharmaprojects 2014; McKinsey analysis

Global pipeline composition, directional technology outlook

Critical mass is building across diverse, clinically useful platforms

Conventional therapies2

(small molecules, non-recombinant vaccines, natural extracts)

Other1

Monoclonal antibodies

Gene therapies

Recombinant proteins (non-mAb)

Cell and tissue therapies

Peptides

1970 1980 1990 2000 2010 FutureNow

RNA, aptamers

Future modalities (not exhaustive)

Messenger RNA

or short RNA

therapies

CRISPR/Cas9

Gene therapy

Multispecific (BiTE,

DART)

Ab fragments

Ab-drug

conjugates

Stapled peptides

Cyclical peptides

DARPins


Breakthroughs in biology --- pathways and targets

CancerUnderstanding and

tracing of clonal

evolution of cancer cells

and tumor heterogeneity

Synthetic biologyAbility to reprogram

single cells with artificial

genetic circuits to

perform simple tasks

Regenerative

medicineDiscovery of minimal set

of drivers that recreate

“stem cell” immortality

CardiovascularDiscovery of microRNA

as a critical regulator of

cardiac and vascular

health

MetabolismUnderstanding of the

growing role of brown

and beige fat cells in

energy efficiency and

insulin sensitivity

Infectious diseaseRealization of

importance of the

microbiome / normal

flora for maintaining

health


Restoring “cultural harmony” with microbes

The Microbiome has been getting

more attention in the popular press

Three types of companies emerging

in microbiome therapeutics

Why I Donated My Stool

Germs Are Us

Bacteria make us sick.

Do they also keep us alive?

Refined fecal

transplant

Defined Microbial

community

Microbiome based

drug discovery

CIPAC

ILLUSTRATIVE


Solving medical non-adherence with the “host’s” machinery

SOURCE: Elbashir and Tuschl, Nature 2001; Lieberman

The Catalytic

RISC RNA Cycle - Potent

and durable silencing.

The same small RNA is used over

and over. The active siRNA strand

is stable in the cell for weeks.

Fewer than1000 siRNAs/cell cause

complete knockdown.


California

Japan

UK

1

4

7

8

1112 15

Toronto

New York

Maryland

Boston

2

56

9

10

1314

Cell manufacturing

Companies

Network and

Consortiums

Research centers

Legend1 California Institute for Regenerative

Medicine

2 Centre for Commercialization of

Regenerative Medicine

3 Centre for Advanced Therapeutic

Cell Technologies

4 CGT Catapult Manufacturing Centre

5 Harvard Stem Cell Institute

6 Harvard University Department of

Stem Cell and Regenerative Biology

7 Lonza Asia

8 Lonza Europe

9 Lonza North America

10 McEwen Centre for Regenerative Medicine

11 Kyoto University Centre for iPS cell

research & application

12 Progenitor Cell Therapy

13 Progenitor Cell Therapy

14 The New York Stem Cell Foundation

15 WuXi AppTec

3

Global community as platform for regenerative medicine

McKinsey & Company 34SOURCE: Expert interviews, McKinsey analysis, Nature

Redefining “medicines” with Electroceuticals

Device engineering hurdles

Basic biology and medical side hurdles

Hurdles to be solved

Near-term Mid- to longer-term

Complex central nervous system

circuits (e.g., stroke, multiple

sclerosis, complicated seizure

disorders)

Smaller, relatively simple

peripheral circuits (e.g.,

blood pressure, insulin

release in diabetics,

chronic localized pain)

Simple, large nerves

(e.g., vagus for

autoimmune or

inflammatory conditions,

discrete spinal cord

injuries)


Embracing innovation to avoid a productivity plateau

Time

“We always overestimate the change that will occur in the next two

years and underestimate the change that will occur in the next ten.

Don’t let yourself be lulled into inaction” – Bill Gates

Inflection point

Economic

downturns

Aggressive payor

pressure

Intense industry

competition

Generics and

biosimilars growth

Increasing rate

of adoption and

connectivity

Individuals are

getting wealthier

More consumers are

entering the market

Aging population

Favorable demographics

Increased invest-

ment and substrate

Improving science /

tech. infrastructure

Increased demand

for evidence

Disruption by

new players

Pasha SarrafPartner, New York

[email protected]


End to strategic sameness?


New models for the future . . .

embracing innovation to avoid a productivity plateau...sep 21, 2016 · embracing innovation to...

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