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CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of McKinsey & Company is strictly prohibited
WORKING DRAFT
Last Modified 9/21/2016 2:02 PM Eastern Standard Time
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
McKinsey & Company 3
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
McKinsey & Company 4
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
McKinsey & Company 5
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)
McKinsey & Company 6
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
McKinsey & Company 7
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%
McKinsey & Company 8
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
McKinsey & Company 9
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
McKinsey & Company 10
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
McKinsey & Company 11
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?
McKinsey & Company 12
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
McKinsey & Company 13
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
1 Productivity index = NPV (revenue) / NPV (development cost), averaged over n=1000 simulations
McKinsey & Company 14
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
1 Productivity index = NPV (revenue) / NPV (development cost), averaged over n=1000 simulations
McKinsey & Company 15
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
McKinsey & Company 16
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
McKinsey & Company 17
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
McKinsey & Company 18
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
McKinsey & Company 19
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
McKinsey & Company 20
Addressing productivity --- A few observations from the field
Surfing the “S curves”
Back to the “Classics” Searching for “Exo”intelligence
Embracing “Pheno”menology
McKinsey & Company 21
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
McKinsey & Company 22
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
McKinsey & Company 23
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
What it is like . . .
Uncoordinated
Stuck
Empire building
Fire-fighting
Local tribes
Finger pointing
Rigid
What it is like . . .
Quick to mobilize
Collaboration
Responsive
Free flow of information
Quick decision-making
Empowered to act
Resilient
What it is like . . .
Risk averse
Standard ways of working
Silos
Decision escalation
Reliable
Centralized
Established
McKinsey & Company 24
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%
McKinsey & Company 25
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
McKinsey & Company 26
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
McKinsey & Company 27
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
McKinsey & Company 28
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
McKinsey & Company 29
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
McKinsey & Company 30
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
McKinsey & Company 31
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
McKinsey & Company 32
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.
McKinsey & Company 33
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)
McKinsey & Company 35
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
McKinsey & Company 37
End to strategic sameness?
McKinsey & Company 38
New models for the future . . .