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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
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