Ten years on
Measuring the return frompharmaceutical innovation 2019

Ten years on| Measuring the return from pharmaceutical innovation 2019

Contents
Foreword

01

2019 results for large cap biopharma companies

02

Executive summary

04

A decade of decline and transition

06

Measuring the return from pharmaceutical innovation

09

The key drivers of the changing R&D model

16

Shaping the future of biopharma innovation

34

Appendix37

Endnotes

40

Contacts

44

The Deloitte Centre for Health Solutions: Turning evidence into action
We are the research arm of Deloitte’s Life Sciences and Health Care
practices. The UK Centre’s team of researchers, working in partnershipwith colleagues from the US Center for Health Solutions, aim to be atrusted source of relevant, timely and reliable insights on emerging
trends, challenges and solutions. We combine creative thinking, robust
research and our industry experience to develop evidence-based
perspectives on some of the biggest and most challenging issues to help
our clients to transform themselves and, importantly, benefit the patient.
At a pivotal and challenging time for the industry, we use our research toencourage collaboration across all stakeholders, from pharmaceuticals
and medical innovation, health care management and reform, to thepatient and health care consumer.
In this publication, references to Deloitte are references to Deloitte LLP,
the UK affiliate of Deloitte NSE LLP, a member firm of Deloitte Touche
Tohmatsu Limited.
B

GlobalData
GlobalData is a global data & insights solution provider who, for over
40 years, has been helping over 4,000 companies worldwide to makemore timely, fact-based decisions. Our mission is to help our clients
succeed and be more innovative by decoding the future and reducing
the noise & uncertainties surrounding the world of today. We do this byproviding market data, competitive insights and end-user perspectives
which are delivered to our clients in an integrated way through a variety ofdifferent tools.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Foreword
Welcome to Ten years on, the tenth annual report fromthe Deloitte Centre for Health Solutions exploring theperformance of the biopharmaceutical industry(biopharma) and its ability to generate returns frominvestment in innovative new products.
Over the past ten years, our Measuring the return from pharmaceutical innovation series has tracked the return on investment that acohort of 12 leading global biopharma companies might expect to achieve from their late-stage pipelines. For the past five years, wehave also tracked the performance of an extension cohort of four, more specialised, biopharma companies. This has enabled us tocompare and contrast performance and deepen our insight into company and pipeline characteristics that drive R&D productivity.
The analysis reveals a systemic, cross-company, decade-long decline in the productivity of R&D in our original cohort and asimilar trajectory for our extension cohort. These findings reflect the R&D challenges of the industry more widely. While individualcompanies do experience short-term successes, the effect of rising costs and declining sales on returns seems inescapable. A
decade of analysis shows that all of our cohort companies have felt the impact of these challenges. This raises critical questions forthe industry, the key one being how prepared are biopharma companies to transform their R&D models? The answer will influence
how companies determine their capital allocations over the next decade.
At the pipeline level, key thresholds have been crossed – more than half of pipelines are now biologics, and more than half of assets
have been externally sourced. The implication remains the same – return on investment will not improve unless R&D productivity
improves. The development of more targeted approaches to drug discovery and development is leading companies to adopt or
optimise the use of much broader, computational technology platforms. New, data-driven R&D models will inevitably emerge. With
data and information driving drug development, we envisage some biopharma companies will become data organisations, whileothers will transition to a leaner, more focused, science-based model with a research footprint within key innovation clusters and a
growing revenue stream from specialty products and biologics.
Within this changing landscape, what has stayed constant over the decade is that companies with deep knowledge of specifictherapy areas consistently earn higher returns than those who go through cycles of re-invention in new therapy areas. This is aconsistent observation that challenges some of the traditional value creation narratives.
While it continues to be a challenge for leaders to unlock R&D productivity, we remain optimistic that the lessons from the last
decade will help biopharma transition to a future where disease prevention and curative therapies transform care and improve thehuman condition. As always, we hope this report is engaging and thought provoking. We welcome feedback and look forward to
discussing the implications of our findings.

Colin Terry
Partner
EMEA Life Sciences
R&D Advisory
Deloitte LLP
colterry@deloitte.co.uk

Neil Lesser
Principal
US Life Sciences
R&D Strategy
Deloitte Consulting LLPnlesser@deloitte.com
01Ten years on| Measuring the return from pharmaceutical innovation 2019

Methodology
Since 2010, our Measuring the return from pharmaceuticalinnovation series has focused on the projected returns
from the late-stage pipelines of a cohort of the 12 largest
biopharma companies by 2009 R&D spend. Our five mostrecent reports also include an extension cohort of four
mid-to-large cap, more specialised companies. We use
these two cohorts as a proxy to measure the industry’s
ability to balance initial capital outlay with the cash inflows
biopharma companies are projected to receive as aresult of this investment.
Our consistent and objective methodology
focuses on each company’s late stage
pipeline (assets that are filed, in Phase
III or Phase II with breakthrough therapy
designation as of 30th April each year)
and measures performance across theoriginal and extension cohorts. We use
two inputs to calculate the Internal Rateof Return (IRR): the total spend incurredbringing assets to launch (based on
publicly available information from audited
annual reports or readily available fromthird-party data providers) and an estimateof the future revenue generated from thelaunch of these assets.

As assets are approved, forecast revenues
move from the late-stage pipeline into thecommercial portfolio, moving out of scopeof our analysis and decreasing the value ofthe late-stage pipeline. The graphic on thefollowing page illustrates our methodology,
showing both the static year-on-year
and dynamic (three-year rolling average)measures of R&D returns.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Late-stage pipeline static IRR and drivers of change in IRR methodology
Basketof assets
for whichpredicted
returns aremeasuredStatic IRR:
Snapchat calculation
based on investment
costs and expected
returns

Discovery

Preclinical

Phase I

Phase II

Phase III

Launch21 year sales forecast(from external supplier)+ submitted
for approval

Transition of new assets
from earlier phases,
in-licenced, acquired

Dynamic IRR:
Illustrates the impact
on underlying levers
on changes in
IRR over time
Source: Deloitte LLP, 2019

Forecast sales fromterminated assets fall out

Existing assets
Sales forecast
up/down

Forecast sales fromapproved and launchedassets fall out*Previously published data have been restated in this report as a result of minor corrections. While thiscreates minor changes in the company and consolidated figures, the trends remain consistent with thedata published originally.

Ten years on| Measuring the return from pharmaceutical innovation 20192019 results for large capbiopharma companies
R&D returns forour original cohortof 12 large capbiopharmacompanies havesteadily declined
since 2010

10.00

9.00
8.00

7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Forecast peak sales per asset
have more than halved since 2010

12

2010

m
75

20

$1
1

m
0m $1188
1
3
$1

2014
201
3

$1403m$13
48m

10.1% 7.6% 7.3% 4.8% 5.5% 4.2% 4.2% 3.7% 1.9% 1.8%

010
12
1
20

17

20

m
77
$14

6
201
2015

$1576mThe average costto bring an asset
to market has
increased by
67% since 2010

8
$1

06
m

$2
16
8m

20
18
2019

$816m
$702m

2011

$551m

2012
2013

$466m

2014

$471m

2015

$416m

2016

$400m

2017

$1981
m

$443m

2018

$407m

2019

$376m
0

02

200

400

600

800

1,000

Ten years on| Measuring the return from pharmaceutical innovation 2019

Large cap biopharma companies
have replenished their late-stage
pipelines in 20192018

Our extension cohort of 4 more
specialised biopharma companies
outperformed the original
cohort in 2019 but are facing
similar productivity challenges
and declining returns

159
assets2019

183
assets

2018

9.3%

2019

6.2%

Forecast sales data suggest antibody therapies are now the mostvaluable drug modality, overtaking small molecules

49%

2010

small
molecules

39%

antibody
therapies

34%

2019

small
molecules

47%

antibody
therapiesTen years on| Measuring the return from pharmaceutical innovation 2019

The projected value of oncology
assets in late-stage pipelines has
doubled since 2010
2010

Companies in our cohorts aresponsoring a smaller
percentage of oncology trials
across the industry

18%

64%

56%

2010

2019

2019

36%
Sources of innovation are changing

33%

acquisitions

Large cap biopharma isincreasingly relying on M&A in
2019, with 33% of forecast sales
derived from acquisitions

26%

co-development

More specialised biopharma arepartnering to access capability andinnovation, with 26% of forecast
sales from co-development

Ten years on| Measuring the return from pharmaceutical innovation 2019

Biopharma companies need to pursue a fundamental shift
in their R&D model

Advanced analytics
and technologies

Biopharma

Earlierpartnering

Digitaltransformation

New operating
models

The futureof health

New capabilities, skill
sets and talent

The future of biopharma R&D
We continue to see afuture for small molecule
research, but companies that
do not shift their operating
models to accommodate
emerging modalities risk
becoming less competitive
Scientiﬁc breakthroughs
will occur at an
exponential pace, buildingon the insights derived fromradically interoperable data

R&D costs will shift fromtraditional discovery and trial
execution to a process
driven by large datasets,advanced computing powerand cloud storage
Advanced analytics andtechnologies will enableend-to-end automation of
R&D, reducing timelines
signiﬁcantly
03Ten years on| Measuring the return from pharmaceutical innovation 2019

Executive summary
Decades of advances in science and technology have driven improvements inhealth care outcomes and influenced stakeholder expectations of the role of thebiopharmaceutical industry (biopharma). However, the past decade has seen increasingpressures undermine the productivity of biopharma R&D, leading to a decade of
decline in the return on investment. At the same time, innovative new treatments arechanging the face of disease management. New treatment modalities and an increasing
understanding of precision medicine have led to the need for new R&D models and afuture where medicine is more participatory, preventative and personalised.
A decade of decline and transition
Our series Measuring the return frompharmaceutical innovation has provided
insight into the state of biopharma R&D
since 2010, analysing the return on
investment that 12 large cap biopharmacompanies might expect to achieve fromtheir late-stage pipelines. Each of ourreports deploys a unique and consistently
applied methodology for assessing
the value from R&D investment. In thefirst four years, our analysis indicated asteady decline in average internal rateof return (IRR), along with an inverse
correlation between IRR and company
size. Consequently, in 2015, we introduced
an extension cohort of four smaller, more
specialised companies and analysed theirpipelines back to 2013. The analysis of
the performance of the late-stage drug
pipelines of the two cohorts provides
a proxy for measuring biopharma’s
ability to balance R&D investment (initialand ongoing capital outlay) with the
cash inflows (drug sales) the industry is
projected to receive as a result of theinvestment. Overall, our analysis showsthat both of our cohorts have seen
significant declines in their expected
returns over the ten years, suggesting thecurrent high-risk, high-cost R&D model isunsustainable.

04

Measuring the return frompharmaceutical innovation
Our original cohort has seen their projected
IRR decline from 10.1 per cent in 2010 to
1.8 per cent in 2019, down 0.1 percentage
points from 2018 and 8.3 percentage
points overall. However, in 2019, whileeight of the 12 companies in our original
cohort improved their returns compared to2018, only one company achieved returns
above five per cent, and the range in values
between the top and bottom performer
has narrowed to its lowest value in ourseries. The IRR for our extension cohort
also declined to its lowest level in 2019,
down from 17.1 per cent in 2015 to 9.3 percent in 2018 to a low of 6.2 per cent in 2019,
mainly due to asset terminations. However,the four extension cohort companies arestill outperforming their larger original
cohort peers.
Our R&D productivity measure is a factorof the cost to develop the assets in thecompany pipeline and the expected sales
from these assets once launched. The
average cost to develop an asset, including
the cost of failure, has increased in six outof nine years. In 2019, our original cohort’s
average cost to develop an asset decreased
from $2,168 million in 2018 to $1,981 million
in 2019 (the cost per asset in 2010 was$1,188 million). Similarly, our extension
cohort’s average cost decreased from$2,805 million in 2018 to $2,422 million in2019 (the cost in 2015 was $1,260 million).

Forecast peak sales per asset also declined
for both cohorts. The original cohort’s
average fell below $400 million for the firsttime, to $376 million in 2019, down from$407 million in 2018 (the average peaksales per asset in 2010 was $816 million).
For our extension cohort, forecast peaksales increased from $1,113 million in 2015
up to $1,165 million in 2018 but decreased
significantly to $658 million in 2019.
Despite the fall in peak sales per asset,
the average cost to develop an asset
decreased for both cohorts because theysuccessfully replenished their late-stage
pipelines with assets from earlier stagesof development or licensing deals. Overall,
the number of late-stage pipeline assets
in the original cohort’s portfolio increased
from 159 to 183, a three-year high andvery close to the ten-year average of 186.5,
while the extension cohort’s portfolio
increased from 23 to 30 late-stage assets.

Ten years on| Measuring the return from pharmaceutical innovation 2019

The key drivers of the changing
R&D model
Several factors are influencing R&D
pipelines, including the increase inbiologics in the pipeline, the length oftime in development (cycle times) andsources of innovation. While the searchfor small molecule therapeutics definedbiopharma R&D for decades, an increased
focus on biologics has led to more diverse
biopharma pipelines. In 2010, traditional
small molecules made up 67 per cent of
our original cohort’s pipeline of late-stage
assets, however, the proportion has
fallen to 43 per cent in 2019. Antibody
therapies now account for 37 per cent of
the pipeline, up from 15 per cent in 2010.
The proportions of other modalities,
including cell and gene therapies,
antisense oligonucleotides, protein-based
therapies, vaccines and synthetic peptides
have changed little in the past decade,despite the increased hype around recent
breakthroughs. However, we expect thatin future years, ‘next gen’ modalities will
increasingly drive biopharma innovation,with biopharma companies likely to
encounter numerous challenges adapting
to the changes needed to develop these
new modalities.
The shift in drug development towards
more scientifically complex modalities andtherapy areas has also affected clinical
trial cycle times. This is despite efforts
from regulators to introduce initiatives
to accelerate drug development andapprovals. Biopharma companies today
are taking longer than ever to bring new
drugs to market, with steady increases
in average cycle time mainly due to theincreasing share of the pipeline focused on
oncology, which has longer average cycle
times compared to other therapy areas.
While biopharma companies have pursued

different approaches to reduce cycle times,
they have had marginal if any impact.
Moving forward, strategies will need
to focus on optimising the clinical trial
process and the use of digital technologies,
including using artificial intelligence toexpedite patient enrolment, improve
protocol design and site selection, andcapture patient reported outcomes anddigital biomarkers.
In 2010, close to half of our original
cohort’s late-stage pipeline was sourced
through external innovation, which
historically launched at higher rates than
the industry benchmark. For the past two
years, over 50 per cent of both our cohorts
late-stage pipelines have been sourced
externally. Furthermore, an increasing
proportion of new molecular entity/new
active substance approvals have comefrom outside our cohorts. Likewise,companies from outside our cohorts aresponsoring an increasing proportion ofclinical trials, rising from 44 per cent in2010 to 57 per cent in 2019. This raises
questions around the sustainability of big
pharma’s current innovation model, andwhether smaller companies may ultimately
take an increasing share of the market bydeveloping and commercialising products
independently.
Indeed, there has been an influx of private
equity and venture capital investment
going into the biotech market, mainly tocompanies focused on new modalities.
Consequently, emerging companies havebeen able to pursue development into
later stages, which in the long run will
make it more difficult for big pharma to
buy innovation. Big pharma will also facecompetition from other non-traditional
competitors. In a future of health driven
by shared and interoperable data,empowered consumers and scientificbreakthroughs, biopharma companies
will need to develop entirely different corecapabilities from today.
Shaping the future of biopharma
innovation
The past ten years of decline in IRR
illustrates quite starkly that new models of
R&D are needed. The growing number ofrich datasets and advances in genomics,
analytics and science more generally
are providing an opportunity for every
biopharma company to decide what typeof R&D model will be most appropriate for
their future sustainability. Data conveners,science and insight engines, and data andplatform infrastructure builders will drive
the future of health for biopharma.
In many ways, we maintain the ‘tempered
optimism’ that characterised our first
Measuring the return from pharmaceuticalinnovation report. We believe thatbiopharma companies can reverse thesubsequent decade of decline that wehave uncovered through our analysis
to deliver a sustainable future for theindustry. A lot rests on learning the lessonsof the past ten years to improve R&D
productivity. However, the continued
challenges around rising R&D costs,
declining expected peak sales, expanding
regulatory requirements, more demanding
reimbursement hurdles and otherchallenges will remain. New technologies
and digital transformation offer biopharma
opportunities in how they conduct R&D
and engage with participants in clinical
trials and with regulators. Biopharma’s
ability to adapt to the futureof health will determine the success of
the industry.

05

Ten years on| Measuring the return from pharmaceutical innovation 2019

A decade of decline
and transition
The past decade has brought massive changes in our knowledge of science andtechnology, which has driven improvements in health care outcomes and affectedstakeholder expectations of the role of the biopharmaceutical industry (biopharma).
While biopharma companies continue to develop increasing numbers of innovative
life-enhancing and life-saving therapies, the cost of developing these therapies has
continued to rise, while paying for new treatments has come under increasing
scrutiny from payers, providers and patients.
At the same time, constraints aroundmarket access and pricing, changing
patterns of demand and continuing
downward pressure on health carebudgets, have presented the industry witha volatile and highly uncertain economic
environment in which to operate, let alone
drive productivity improvements.
The changing prevalence of chronic
diseases, including the rise of age-relateddiseases such as dementia and cancer,
has led to many people living longer
but with more complex comorbidities.
Our increasing knowledge of science –particularly advances in genomics – andthe fact that we know drugs do not work
the same way in everyone has uncovereda growing number of areas of unmet
need. As a result, the demand on existing
drugs and the search for new therapies
has increased, and, at the same time,
attracted the attention of payers andpolicy maker as to the cost-effectivenessand affordability of new treatments.

06

The rationale for a new metric forevaluating capital allocation to R&D
Ten years ago the life sciences industry
was beginning to experience afundamental productivity challenge, as
growth in investment in R&D outpaced
the growth of sales without resulting in
an increased output of new medicines. In
the first report in our series on Measuring
the return from pharmaceutical innovation,
we hypothesised that the historical‘input-led’ approach of investing 15-20
per cent of sales revenue into R&D was
no longer fit for purpose. Moreover, amore effective method for assessing thevalue from R&D investment that tooka ‘whole R&D business’ approach wasneeded.1
Consequently, we introduced a uniquemodel for assessing the Internal Rateof Return (IRR) as a way of measuring
the performance of the top 12 global
biopharma companies by 2009 R&D
spend. Each of our annual reports
since then has used this consistent andobjective methodology to calculatechanges to the projected IRR of thelate-stage pipelines of these leading
biopharma companies in an attempt todetermine what key drivers are impacting
these changes.

Our first report showed that the top 12
biopharma companies could expect a
positive return on their investment in R&D.
At 10.1 per cent, the average IRR of the 12companies was above our calculation of
the group average weight adjusted costof capital (WACC) of seven per cent, with
only one company expected to generate
returns that fell below this WACC. We
highlighted the growing complexity ofdrug development compared to the 1990s
and showed a steep rise in the cost of
product innovation that was outpacing
commercialisation success rates, resulting
in companies reconfiguring how they
approached drug development. However,
our findings suggested a ‘tempered
optimism’ about the future, while
recognising that any adverse impacts,
such as rising R&D costs, expanding
regulatory requirements, more demanding
reimbursement hurdles and incremental
austerity-related price cuts would exert asignificant downward pressure on IRR andfuture success.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 1. Overview of past report findings, 2010-18
Year

Title

Average
R&D cost($ millions)Averagepeak sales($ millions)

IRR
(%)

Key conclusions

2010

Is R&D
earning its
investment?

1,188

816

10.1

a ‘tempered optimism’ about the future, recognising that adverse
impacts could decrease IRR and future success2011

Is R&D
earning its
investment?

1,310

702

7.6

significant movements in IRR, biopharma need greater rigour incapital allocation decision making, more intense collaboration
with peers and with payers, and to simplify the fundamentals of
R&D operations

2012

Is R&D
earning its
investment?

1,175

551

7.3

a mixed picture of performance; companies that are successful
in the business of R&D will be effective in marshalling the best
science and advances in diagnostics, and in deploying a flexible,
collaborative development model that focuses early on gathering
evidence of value

2013

Weathering
the storm?

1,348

466

4.8

significant variation in cohort performance, but cohort leaders
appeared to be weathering the storm; investment in R&D remained
a challenging endeavour

2014

Turning a
corner?

1,403

471

5.5

slight uptick in IRR; company size appeared to inversely correlatewith R&D returns, reinforcing view that smaller, more dynamic andflexible R&D units were better equipped to confront the challengesof biopharma R&D

2015

Transforming
R&D returns
in uncertain
times

1,576

416

4.2

extension cohort introduced and were more successful; original
cohort should use their scale and capability on a more focused
set of TAs where they can be a market leader and gain competitive
advantage

2016

Balancing the
R&D equation

1,477

400

4.2

key strategic decisions around TA focus, product strategy and R&D
programme design could increase pipeline value; thinking small,balancing staffing and outsourcing, lifting the burden of data
complexity could reduce the cost to launch2017

A new future
for R&D?

1,806

443

3.7

improving projected returns continues to be challenging, but
numerous examples of innovation demonstrate biopharma’s
resilience and project optimism for the future. Biopharma canincrease returns if it embraces advanced technologies acrossthe value chain

2018

UnlockingR&D
productivity

2,168

407

1.9

projected returns declined to lowest level due to internal andexternal productivity challenges; traditional ways of working are
shifting in biopharma R&D, and companies need to transform
digitally and adopt new ways of working

Source: Deloitte LLP, 2019. *Previously published data have been restated in this report as a result of minor corrections. While this creates minor changes in thecompany and consolidated figures, the trends remain consistent with the data published originally.
07

Ten years on| Measuring the return from pharmaceutical innovation 2019

A decade of analysis has yielded a
wealth of insight into R&D
Since then we have published nine further
reports, including this, our tenth report.
Figure 1 summarises our focus andconclusions over the past nine years.
In 2014, our analysis suggested thatcompany size appeared to inversely
correlate with R&D returns. This reinforced
the prevailing view at that time that smaller,
more dynamic and flexible R&D units were
better equipped to confront the challengesof biopharma R&D.
To test this hypothesis, we introduced
an extension cohort in 2015, comprisingfour mid-to-large cap, more specialised
companies and analysed their pipelines
dating back to 2013 to gather insights
about the extension cohort’s approachand the value of therapy area (TA) focus.
We found that by concentrating R&D
efforts in areas of significant unmet need,
the extension cohort had an average IRRof 17.1 per cent (outperforming by far theoriginal cohort’s returns of 4.2 per cent).
We therefore suggested that companies
in our original cohort should consider

using their scale and capability on a more
focused set of TAs where they can bea market leader and gain competitive
advantage. Moreover, that future winners
in R&D would be companies that exhibit
an end-to-end understanding of disease
areas, patient behaviours, and improvetheir targeting and delivery mechanisms.
In recent reports, our analysis has shown
that, despite launches of many successful
products, the long-term outlook for theindustry is increasingly challenging. Whilethe extension cohort has consistently
outperformed the original cohort, it
has also seen significant declines in its
returns, and the challenges that once only
plagued our original cohort companies
are now systemic across the industry.
More specifically, new drugs are becoming
much more expensive to develop andare targeting much smaller patient
populations. Biopharma companies
therefore need to reduce the costs of
their R&D or increase the value of theirlate-stage pipeline assets to improve
productivity.

“In recent reports, our analysis
has shown that, despite
launches of many successful
products, the long-term outlook
for the industry is increasingly
challenging.”

08

In the future, biopharma’s approachto R&D will need to adapt
As health care increasingly moves fromtreatment to prevention and even cure,building robust evidence to back healthcare claims, as well as improving
relationships with patients based on trust,is essential to obtaining both regulatory
approval and patient engagement.
In addition, the past ten years have seena growing number of ‘new entrants’competing with big pharma for patientsand patients’ data, including developing
new approaches to treatment. Big techcompanies, as well as innovative startups, are using complex digital tools andalgorithms to try and help patients stayhealthy and manage their conditions
more effectively. Meanwhile, biopharmacompanies are beginning to embrace atruly patient-centric approach, establishingalternative approaches to disease
management and a more proactive
approach to regulation.
The latter part of this decade hascontinued to be increasingly challenging
but also very innovative. The rest of thisreport provides the results of our 2019analysis of the return on investment on
R&D and evaluates some of the key drivers
that could help deliver a sustainable future
for biopharma R&D.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Measuring the return frompharmaceutical innovation
Analysis from the first nine years of our Measuring the return from pharmaceuticalinnovation series concluded that a transformational change in R&D productivity isrequired to reverse declining trends in R&D returns across the biopharma industry.
Analysis from this, our tenth report, shows that this conclusion is still true today.
The original cohort’s projected
returns have declined slightly to
1.8 per centIn 2019, the consolidated average IRR for
the original cohort has declined to 1.8 percent – a slight decrease of 0.1 per centfrom 2018, but a decrease of 8.3 per centfrom 2010 (Figure 2). This represents anaverage decline of 0.83 per cent per year.
The wide variations in performance
between individual companies in theoriginal cohort that have been a feature ofprevious analyses are no longer evident,
as the range in values from the top andbottom performer has declined from 10.4

per cent in 2018 (top performer: 7.5 per
cent, bottom performer: -2.9 per cent) to
7.1 per cent in 2019 (top performer: 5.1 per
cent, bottom performer: -2.0 per cent). In
addition, while eight of the 12 biopharmacompanies in our original cohort improved
slightly from 2018, only one company
achieved returns above five per cent.
On a three-year rolling average basis,
the average IRR of the original cohort isnow tracking at 2.5 per cent for 2017-19(Figure 26 in Appendix). Please see thesection dedicated to the extension cohort
beginning on page 14 for an analysis of
their performance.

Figure 3 shows the aggregate drivers ofchange for the original cohort between
2010 and 2019, our calculation of theyear-on-year IRR and illustrates the key
drivers of change between 2018 and 2019.
Year-on-year drivers of change in IRR for
the original cohort over the past decade(2010-19) are shown in Figure 27 in the
Appendix.

Figure 2. Return on late-stage pipeline, 2010-19 – original and extension cohorts
20
Extension cohort

Static IRR (%)

10

17.4

17.7

Top performer
16.1

10.1

11.0
7.6

7.3

4.8

12.5
9.3

5.5

5

4.2

4.2

6.2
3.7

1.9

1.8

Original cohort

15

Third quartile
Mean average
MedianFirst quartile0

Bottom performer-5

Original cohort
-10
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: Deloitte LLP, 201909

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 3. Drivers of change in IRR 2010-19 consolidated, 2010-19 year-on-year
and 2018-19 – original cohort
35

+20.5

+0.7

-20.7

30

IRR (%)

25
20
15
10

10.1

-5.6
-3.4

5

+1.0

-0.8

Non R&D
costs

Other

1.8

0
2010

New

Existing

Approved

TerminatedR&D costs

2019

12
10

10.1

7.6

IRR (%)

8

7.3

6

5.5
4.8

4.2

4

4.2

3.7
1.9

1.8

2018

2019

+0.2

0.0

1.8

Non R&D
cost

Other

2019

2

0
2010

2011

2012

2013

2014

2015

2016

2017

6

5

+0.7

-2.3

+2.7

IRR (%)

4

3
-0.8
2

-0.7

1.9

1

0
2018

New

Existing

Approved

TerminatedSource: Deloitte LLP, 2019
Due to rounding, numbers presented throughout this document may not add up precisely tothe totals provided, and percentages may not precisely reflect the absolute figures.

10

R&D Cost

Ten years on| Measuring the return from pharmaceutical innovation 2019

Between 1 May 2018 and 30 April 2019,the original cohort launched a total of
36 approved assets, with forecast total
sales of $152 billion. This represented a
2.3 percentage point decline in projected
returns, the fourth highest decrease due
to approvals since our analysis began in2010.
Set against successful approvals, latestage R&D continues to be inherently risky,which is underlined by the decrease in
IRR due to late-stage failures. In 2017 and2018, assets removed from the pipeline
due to terminations contributed to a 0.7
percentage point decline in IRR. This year,
terminations have contributed to a 0.8
percentage point decline in IRR. While thisis not noticeably different from previous
years, the overall effect of terminations
has been responsible for a decline of 5.6
percentage points since 2010.

A consistent trend highlighted
throughout our Measuring the return frompharmaceutical innovation series has
been that, while companies continue
to innovate, they have been unable toreplenish late-stage pipelines at a ratethat compensates for the successful
approval and flow of value into thecommercial pipeline and loss throughlate-stage attrition. This year has seen
an increase of 2.7 percentage points due
to 60 new assets entering the pipeline,
which is 1.1 percentage points higher
than in 2018. These assets have forecast
lifetime sales of $332 billion.

Similar to 2018, and for the fourth time inour series, the original cohort has beensuccessful in de-risking and increasing
the value of projected returns fromexisting late-stage pipeline assets, with
a 0.7 percentage point increase in 2019.
This increase in forecast revenues fromexisting assets has been largely driven bypositive clinical trial data, class effect anddelays to loss of exclusivity in forecasting
assumptions.

Figure 4. Average number of late-stage pipeline assets, 2010-19 – original and extension cohorts
30
Extension cohort
Top performer
16

20
17

15

16

16
15

15

10

6

6

15

16

7

6

6

13

6

15

8

5

Original cohort

Number of assets

25

Third quartile
Mean average
MedianFirst quartile

Bottom performer
Original cohort

0
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: Deloitte LLP, 201911

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 5. Overall impact of pipeline factors on change in IRR, 2010-18 and 2018-19 – original cohort
18

15

12
IRR (%)

10.1

-2.0

9

-4.2

6
-2.0
3

1.9

+0.9

-1.0

No. assets

Peak sales

-0.1

1.8

Other

2019

0
2010

No. assets

Peak sales

Other

2018

Source: Deloitte LLP, 2019
Due to rounding, numbers presented throughout this document may not add up precisely to the totals
provided, and percentages may not precisely reflect the absolute figures.

Declining returns are the result ofinternal and external productivity
challenges
In recent years, we have seen declines
in the number of assets in the original
cohort’s late-stage pipeline, whichreached a low of 159 in 2018, an averageof 13.25 per company. However, there
is significant variation between thecompanies (from four to 21) (Figure 4).
This year the number of late-stage assets
has increased to 183, a three year highand very close to the ten year averageof 186.5, corresponding to an average of
15.25 assets per company, with the range
narrowing from eight to 22. The increasein the number of assets in late-stage
development has contributed to a 0.9
percentage point increase in IRR between
2018 and 2019 (Figure 5).

12

The average cost to develop an
asset has decreased due to pipeline
replenishment
Our cohort companies spent $79 billion
on R&D in 2019, corresponding to anincrease of 17 per cent in underlying R&D
expenditure since 2010. However, due
to the increase in the number of pipeline
assets, the average cost to develop an
asset in 2019 is $1,981 million, a decreaseof $187 million from 2018 (Figure 6).
At constant late-stage asset numbers(159 from 2018), the average cost per
asset would have increased to $2,280
million, an increase of $112 million.

On a three-year rolling average basis,
the average R&D cost is now tracking at$1,971 million for 2017-19 (Figure 28 inAppendix).
With the decline in average cost to
develop an asset, we also see a decline
in the range from the top and bottom
performers from our cohort, although
this variance is still significant across ourcohort companies.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 6. Average R&D cost to develop a compound from discovery to launch, 2010-19– original and extension cohorts$4,500Extension cohort$4,000Top performer$2,805$3,000$2,422$2,500$2,111$1,910$2,000$1,500$1,310$1,188$1,348$1,403$1,576$1,806$1,175$1,000$2,168

Original cohort

Cost to launch ($m)$3,500Third quartile
Mean average
MedianFirst quartile$1,981$1,477$1,034

Bottom performer$1,260$951

Original cohort

$500
$0
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: Deloitte LLP, 2019

Forecast peak sales decline
slightly in 2019
The decrease in the average forecast peaksales per asset has, and continues to be,
the greatest reason for the decline in IRR ofthe pipeline factors highlighted in Figure 5.
This year has seen a decline in average
forecast peak sales per pipeline asset from$407 million in 2018 to $376 million in 2019
(Figure 7). The range in average forecast
peak sales this year is the lowest is has
been over the last ten years. On a threeyear rolling average basis, average forecast
peak sales per asset is now tracking at$408 million for 2017-19 (Figure 29 inAppendix).

“The decrease in the average
forecast peak sales per asset
has, and continues to be,
the greatest reason for the
decline in IRR.”

13

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 7. Average forecast peak sales per pipeline asset, 2010-19– original and extension cohorts
1,600
Extension cohort

1,400$1,221

1,200$1,047$1,113

$952

1,000

$910

$702

800

$551

$466

$658

$471

600

$416

$ 400

$443

$407

$376

Original cohort

Peak sales ($m)$816

Top performer$1,165

Third quartile
Mean average
MedianFirst quartile

400
Bottom performer

200

Original cohort

0
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source: Deloitte LLP, 2019

The extension cohort is startingto face productivity challenges
similar to the original cohort
The extension cohort has seen a much
more dramatic decline in IRR than theoriginal cohort in 2019. However, thefour extension cohort companies are still
outperforming their larger original cohort
peers, with an IRR of 6.2 per cent, a value
not bettered by the original cohort since2012 (Figure 2).
In our 2018 analysis, the extension cohort
saw an increase in its average cost to
bring an asset to market rise to $2,805
million. In 2019, this cost remained above$2 billion for the third year running but
declined to $2,422 million (Figure 6).
The primary reason for this decline forthe extension cohort is due to theincrease in asset numbers across thecohort, which increased from an averageof six per company in 2018 to eight percompany in 2019 (Figure 4), reversing a
trend we have seen over recent years.

14

Average forecast peak sales for theextension cohort fell dramatically below$1 billion for the first time since 2016,
to $658 million from $1,165 million in2018 (Figure 7). This is the third timethat the extension cohort has failed toachieve average forecast peak sales ofblockbuster status ($1 billion or more)in our analysis, but this stark contrast to
recent years is mainly due to one forecast
high-value asset failing to gain regulatory
approval, and at the cut-off point of ouranalysis was considered terminated.

Our 2018 analysis saw a 0.8 percentage
point improvement in IRR due to existing
assets that remained in the pipeline
year-on-year.2 The primary driver of thisincrease was positive trial data, and to alesser extent, class effect and competitor
failure. In 2019 we have seen a similar
trend, resulting in a 1.6 percentage
point increase in IRR due to existing
assets (Figure 8). However, IRR declined
in 2019 due to terminated assets of 4.2
percentage points, corresponding tothree assets, which is the largest decline
due to terminations recorded.

“The extension cohort has seena much more dramatic decline
in IRR than the original
cohort in 2019.”

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 8. Drivers of change in IRR, 2018-19 – extension cohort
16
14

+1.6
+2.6

12

IRR (%)

10

-1.6
-4.2

9.3

8

-0.5

-0.3

-0.6

6

6.2

4
2
0
2018

New

Existing

Approved

TerminatedR&D
costs (pure)

Non R&D
costs

Other

2019

Source: Deloitte LLP, 2019
Due to rounding, numbers presented throughout this document may not add up precisely tothe totals provided, and percentages may not precisely reflect the absolute figures.

“Average forecast peak sales for theextension cohort fell dramaticallybelow $1 billion for the first timesince 2016, to $658 million from
$1,165 million in 2018.”

15

Ten years on| Measuring the return from pharmaceutical innovation 2019

The key drivers of thechanging R&D model
Biopharma’s future will be focused on developing therapies for the multiple diseases
that still lack treatments and creating more precise treatment regimens that target
smaller populations or individual patients. Developing these treatments brings newchallenges. Biologics that target smaller groups of patients are more expensive andtime-consuming to develop, and inherently generate less revenue. To improve R&D
productivity, there is also a need to shorten clinical cycle times and develop better
understanding of the impact of different sources of innovation and the implicationsof capital markets.
The growing importance of biologics
Until the 1990s, with the exception of
vaccines, drug development focused
almost entirely on chemically synthesised
small molecule therapeutics, which still
make up 90 per cent of drugs on themarket today.3 These products tend totarget large populations of patients in a‘one-size-fits-all’ approach and earned
significant sales revenue. In 2010,
biopharma companies came underincreasing pressure to compensate forthe significant loss of patent protection
around many of their products,
particularly small molecule products, due
to competition from generics.4,5 Deloitte’s
2010 report, The future of the life sciencesindustries: Aftermath of the global recession,
estimated that the biopharma industry
could lose as much as $60 billion inrevenue due to drugs going off patent in2010 and 2011.6 By 2018, generics madeup more than 80 per cent of the volumeof drugs dispersed around the world, andby 2022 are expected to make up almost
30 per cent of total drug sales worldwide.7

16

While small molecules are still a vital
aspect of most biopharma companies’
late-stage pipelines, the competition
from generics – alongside advances inscience and technologies – have driven
biopharma companies to seek otherdrug modalities including innovative
new biologically based treatments(biologics). This has changed the overallcomposition of the pipeline. However,while biologics have higher specificity
and can be more effective, they target
smaller populations, are more expensive
to produce and, consequently, cost more.
They are therefore prescribed much lessthan small molecules. In 2017, in the US,
biologics accounted for only two per centof all prescriptions, but accounted for 37
per cent of net drug spending.8
Only six of the 21 drugs approved by the
US Food and Drug Administration (FDA)
in 2010 were biologics. Similarly, of the 29
drugs approved by the FDA in 2011, only
six were biologics. Overall, between 2010
and 2017, the FDA approved 63 biologics
compared to 199 small molecules.9 In
2018, however, 17 of the 59 new drugs
approved by the FDA were biologics,
and through 31st October 2019, eight ofthe 33 drugs approved so far have been
biologics.10

In our 2010 analysis, 67 per cent of theassets in our original cohort’s pipeline
were traditional small molecules; in our2019 analysis, the proportion of small
molecules had fallen to 43 per cent(Figure 9). Antibody therapies, which
made up 15 per cent of the assets in ourcohort’s pipeline in 2010, now account for
37 per cent of the pipeline. Interestingly,
there has been little change in the overall
percentage of other modalities in thepipeline in the past decade, including
cell and gene therapies, antisense
oligonucleotides, protein based therapies,
vaccines and synthetic peptides (Figure 9).

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 9. Pipeline focus by modality, 2010-19 – original cohort
100%

16%
14%
12%

90%

10%
8%
6%

80%

4%
2%

Percentage of assets in the pipeline

70%

0%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

60%

50%

40%

30%

20%

10%

0%

2010

2011

2012

2013

2014

2015

2016

Traditional Small Molecules

Antibody Therapies

Antisense Oligonucleotides

Cell and Gene Therapy

Protein Based Therapies

Vaccines

2017

2018

2019

Synthetic Peptides

Source: Deloitte LLP, 201917

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 10. Percentage of total forecast sales per modality by year, 2010-19 – original cohort
100%

16%
14%
12%

90%

10%
8%
6%

80%

4%
2%

70%

0%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Total expected sales

60%

50%

40%

30%

20%

10%

0%
2010

2011

2013

2014

2015

2016

Traditional Small Molecules

Antibody Therapies

Antisense Oligonucleotides

Cell and Gene Therapy

Protein Based Therapies

Vaccines

Source: Deloitte LLP, 2019

18

2012

2017

2018
Synthetic Peptides

2019Ten years on| Measuring the return from pharmaceutical innovation 2019

In addition to the changing ratio of small
molecules to biologics, another notable
point is the percentage of total forecast
sales per modality by year (Figure 10). In
2010, small molecules made up 49 percent of the total proportion of forecast
sales, while in 2019 this percentage
has dropped to 34 per cent. Antibody
therapies made up 39 per cent of total
forecast sales in 2010, jumping to 47 percent in 2019.
Today, biopharma is facing a biologics
patent cliff, leading to the emergenceof biosimilars, which is expected to put$251 billion in sales of biologics at risk
between 2018 and 2024.11 Biosimilars
take much longer than generics todevelop, as they still have a relatively
challenging regulatory pathway and are
therefore still expensive to develop andbuy.12 Nevertheless, in 2018 biosimilars
were making notable inroads.13 Market
forecasts suggest the biosimilar market
will to grow at 32 per cent compound
annual growth rate (CAGR) from 2018-23,
with 42 per cent of the growth comingfrom Europe.14‘Next gen’ modalities will increasingly
drive innovation in biopharma
The progression of the drug development
paradigm to include biologics has hada significant impact on the biopharma
business model. While small molecules
can be produced easily in large, uniform
quantities through a well-definedprocess, production of biologics is much
more complicated and yields a muchsmaller quantity of active drug. Scaling
up the production of biologics is also
more difficult, as maintaining purity andreducing batch-to-batch variability ischallenging.15‘Next gen’ therapies take thesedifferences a step further, and althoughbiopharma companies are increasingly

Figure 11. Proportion of cell and gene therapy Phase I-III trials sponsored bycompanies within our cohorts vs industry total
700

Deloitte’s view

30%

600

506
26.2%
25%

Number of cell / gene therapy trials

500

22.7%
400
20%

18.6%
300

221

Ratio of cohort / industry total %The changing proportion ofmodalities in biopharma pipelines
reflects our increasing knowledgeof science and the drive todevelop treatments for diseases
for which there is no treatment.
Small molecules still make up the
majority of our original cohort’s
pipeline, but antibody therapies
are now expected to deliver themost revenue. While the increasing
availability of biosimilars is a morecost-effective way of developing
new assets and improving patient
access to innovative medicines, theirintroduction increases competitionand puts significant biopharma
revenue at risk, albeit at a premium
to small molecule generic versions.
Biopharma companies will need toinnovate to replace this lost revenue
with new products, specifically bydeveloping new, more cost-effective
approaches to biopharma R&D.

focusing on these modalities, particularlycell and gene therapies, they facenumerous challenges adapting to thedevelopment and manufacturing of theseproducts (Figure 11). In the same waythat biologics generally target smaller
populations, ‘next gen’ therapies often
target individual patients – sometimes
as single treatments – resulting intruly personalised medicine. Recently,
ex vivo (where cells are geneticallymodified outside the body) cell and genetherapies have generated considerable
excitement on their potential to curepreviously incurable diseases. However,these therapies have also been subject
to increasing regulatory and healthtechnology assessment scrutiny, including
significant controversy over affordability.

200
141

15%

100

0

41

115

37

Phase I

Phase II

Phase III

Cohort company

Total industry

10%

Ratio cohort/total

Source: Deloitte LLP, 201919

Ten years on| Measuring the return from pharmaceutical innovation 2019

Nevertheless, ex vivo cell and genetherapy sales are forecast to reach $3
billion in 2022, spurred by as many as
200 investigational new drug applications
each year by 2020, and 15 to 20 approvals
each year by 2025. The largest shareof this market currently comes fromchimeric antigen receptor T cell (CAR-T)
therapies (Case study 1). Similarly, genetherapies (Case study 2) and antisense
oligonucleotides (Case study 3) are also
poised to impact the biopharma industry.
These therapies are good exemplars of
the challenges biopharma companies facewith ‘next gen’ therapies.

Mitigating strategies include improving:• patient access: ‘next gen’ therapies arehighly customised, clinically intensive
and costly. Biopharma companies
would benefit from identifying the
combination of factors that leadto successful treatment throughinformatics, engaging early and oftenwith regulators, demonstrating thetreatment’s value to patients andoffering pricing consistent with thatvalue17• supply chain and manufacturing:cell and gene therapies require a
specialised, patient-centric, clinicallyconnected value chain and distribution
model prior to commercialisation.
Non-traditional partners and sourcesof talent may be able to help bridge
capability gaps18• customer and patient engagement:
engagement is critical for success withcell and gene therapies. Biopharmacompanies should offer patients accessto treatment and logistics support
through a technology-enabled multichannel model, and ensure cliniciansin specialist treatment centres receive
appropriate training and ongoingcertification19• health care provider (HCP)
networks: growing a network of
HCPs and selecting treatment sites
are critical for cell and gene therapies.
Biopharma companies should consider
clinical and business factors whenchoosing treatment centres, develop
partnerships with centre personnel tounderstand workflow issues and areasof desired support, and align treatment
protocols across manufacturers.20

Case study 1
CAR-T therapies use a patient’s own immune cells to fight tumours
In CAR-T therapies, T cells are isolated from a patient and genetically modified to target cancer cells. After a period of
growth and expansion, the modified T cells are infused back into the patient, where they target and kill cancer cells.
Response rates have been as high as 70-90 per cent, and some patients have experienced greater than one-year
remissions.21 However, these treatments are high-risk, and adverse reactions can be life threatening, requiring specialised
treatment centres with specially trained clinical staff.
The first two CAR-T therapies were approved by the FDA in 2017:• Kymriah® (tisagenlecleucel) – for paediatric acute lymphoblastic leukaemia (ALL). The European Commission granted
marketing authorisation to Kymriah® in August 2018, and the National Institute for Health and Care Excellence (NICE)
recommended its addition to NHS England’s Cancer Drugs Fund in September 2018. In January 2019, NICE recommended
Kymriah® for adults with relapsed or refractory diffuse large B-cell lymphoma (DLBCL).
• Yescarta® (axicabtagene ciloleucel) – for advanced lymphoma in adults. The European Commission granted marketing
authorisation to Yescarta® in August 2018, and the NICE recommended its addition to NHS England’s Cancer Drugs Fundin October 2018.
Although to date the FDA has approved only two CAR-T therapies, a third CAR-T therapy for lymphoma, lisocabtagene
maraleucel, is currently in Phase III clinical trials and, if approved, the combined revenue from all three CAR-T therapies ispredicted to reach $2.4 billion in 2027.22 Furthermore, there are now over 600 clinical trials involving CAR-T therapies.23

20

Ten years on| Measuring the return from pharmaceutical innovation 2019

Case study 2
Gene therapy breakthroughs
Gene therapy involves the introduction of genetic material into specific patients to treat underlying genetic causes of
disease. Despite a lengthy and convoluted journey from research to clinical application, recent advances in gene editing
technologies and the development of delivery vectors are now allowing gene therapies to bring new hope to patients.
In December 2017, the FDA approved Luxturna® (voretigene neparvovec), a gene therapy drug for the treatment of biallelic
RPE65 mutation-associated retinal dystrophy. The RPE65 gene encodes an enzyme that is involved in the biochemical
reactions that occur during light capture in the retina. Inherited mutations to RPE65 are rare, but they can lead toprogressive vision loss and eventual blindness, usually by the time patients are young adults. Luxturna® was the first
directly administered, adeno-associated virus vector-based gene therapy approved by the FDA that targets a disease caused
by mutations in a specific gene. It was approved by the European Medicines Agency (EMA) in September 2018 and, a year
later in September 2019, NICE recommended the use of this gene therapy product.24,25 It is now expected that up to 100
patients currently living with this retinal dystrophy in the UK will have access to this treatment as soon as January 2020.26
More recently, in June 2019, ZYNTEGLO® gained approval by the EMA for the treatment of patients (aged 12 years and older)with transfusion-dependent β-thalassemia (TDT). Patients with this rare genetic disease, which is caused by mutations inthe β-globin gene, have reduced or absent levels of haemoglobin, and require lifelong regular blood transfusions to lessen
the chronic anaemia and, ultimately, survive.27,28 ZYNTEGLO®’s therapeutic approach makes use of autologous CD34+ stem
cells that have been genetically modified to contain the working β-globin gene. This authorisation for European marketing
was the fastest assessment of an advanced therapy medicinal product (ATMP) to date, having also benefited from the
EMA’s Priority Medicines (PRIME) programme.29

Case study 3
Antisense oligonucleotide therapies offer
new hope to patients with genetic disorders
Genetic sequencing is often critical in diagnosing rare diseases, manyof which are fatal due to lack of available treatments. However, in thedevelopment of milasen, researchers at Boston Children’s Hospital andHarvard Medical School designed, tested and manufactured an antisense
oligonucleotide drug tailored to a particular patient within one year of firstcontact with them. The patient suffered from Batten disease, an inherited
neurodegenerative condition with initial symptoms of deterioration in apatient’s ability to speak, see and walk, which normally has no cure and isfatal. In sequencing the patient’s individual genetic code, the researchers
found a mutation that altered the assembly of an important housekeeping
gene by creating a new, unwanted ‘splice site.’ In milasen, researchers
designed an oligonucleotide to suppress the unwanted splice site and restore
normal assembly of the gene, enabling the patient to produce functional
proteins that had previously been lacking. While not a cure, milasen has led
to reductions in the frequency and duration of seizures, and it
is believed to be the first example of a personalised therapy developed
and approved for a single patient.30

Deloitte’s view
In the future, ‘next gen’ therapies
will offer biopharma companies
many opportunities for innovation.
However, this will require significant
shifts in how biopharma companies
function. They will not only have todevelop new capabilities, but alsocollaborate across all aspects of thebiopharma value chain to ensure
they can bring ‘next gen’ drugs tomarket efficiently and effectively.
These shifts will also drive widerchanges in the biopharma industry,including moving from intervention
to prevention, or treatment tocure. If companies fail to shift theiroperating models to cope with thesechallenges, they risk continuing todrive an imbalance between invested
costs and, despite high per-patient
prices, cash inflows.

21

Ten years on| Measuring the return from pharmaceutical innovation 2019

The ability to collect a greater volume
and variety of data, including genomics,
imaging, digital health data and patientreported outcomes, has expanded
trial protocols which now include
requirements to collect ‘non-coredata’ for tertiary and exploratory data
points, beyond what is needed forregulatory filings or to test primary study
hypotheses.31
At the same time, biopharma companies
have been finding it increasingly
difficult to recruit patients that meetthe selection criteria for their trials.
Traditional recruitment mechanisms
employed by biopharma have led to an
86 per cent failure rate of trials in meeting
their recruitment timelines, and one-thirdof pivotal (Phase III) trials that requirelarger patient cohorts fail owing toenrolment issues.32,33
A shift in drug development efforts
towards more scientifically complex
therapy areas (such as oncology,
immunology and rare diseases) has
added to recruitment complexity. Often,
key inclusion criteria involves identifying abiomarker, a measurable indicator of theseverity or presence of disease. In recent
years, close to 60 per cent of all trials and80 per cent of oncology trials require
some form of biomarker data as part ofenrolment criteria.34

22

Figure 12. Average clinical cycle times, 2014-19– original and extension cohorts (combined)
6.7

6.61
6.6

6.64

6.57
6.58

6.5

Years

Reducing cycle times is key to the
future of R&D
Across the industry, clinical trial cycle
times have continued to grow (despite asmall dip in 2014) (Figure 12). Companies
today are taking longer than ever tobring new drugs to market, with complex
protocol design and recruitment delays
two key factors driving up average cycle
times from Phase I to launch.

6.4

6.39

6.3

6.2
6.15

6.1

2014

2015

2016

2017

2018

2019

Source: Deloitte LLP, 2019

“Traditional recruitment
mechanisms employed bybiopharma have led to an
86 per cent failure rate of trials inmeeting their recruitment
timelines, and one-third of pivotal(Phase III) trials that require larger
patient cohorts fail owing toenrolment issues.”Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 15 shows average cycle times
for oncology are longer than other TAs
generally, and has grown in 2019, likely
attributed to increasing complexity of
protocol design as well as increased
competition to recruit eligible patients.
Meanwhile, cycle times for other TAs,
including central nervous system (CNS),
infectious disease, metabolic disorders
and cardiovascular (CV) have stayed
relatively flat or declined.

Figure 13. Late-stage pipeline composition by therapy area, 2010-19– original cohort
100%

90%

80%

Percentage of late-stage pipeline revenue

The growing number of therapies targetedat the same biomarkers within a therapy
or disease area has also resulted in evenmore competition for trial participants,
slowing down patient enrolment. Thisis especially true in oncology, where
GlobalData analysis reveals that industrywide global clinical trials in immunooncology for 2008–2017 has increased at
a CAGR of 17 per cent over the 10-year
period.35 Our analysis shows that anincreasing share of the pipeline of ouroriginal cohort is focused on oncology,
growing from 18 per cent in 2010 to 36 percent in 2019 (Figure 13). Interestingly, ourtwo cohorts are sponsoring an increasing
share of industry wide oncology clinical
trials (Figure 14).

70%

60%

50%

40%

30%

20%

10%

0%

2010

Other

2011

2012

2013

Respiratory and immunology

Central nervous system

Metabolic

2014

2015

2016

Infectious disease

2017

2018

2019

Cardiovascular

Oncology

Source: Deloitte LLP, 2019

Figure 14. Cohort company sponsorship of oncology clinical trials, 2010-19– original and extension cohorts (combined)
40
70%

34%

Percentage ofProportion
trials active
theofduring
pipeline(%)period

35
65%

33%

30
60%
55%
25

23%

22%

20%

50%
20
45%

15
40%
10
35%
30%
5
2010

2011

2012

Phase II

2015
Phase III

2013

2014

2015

2016

2017

2018

2019

Report year

0
Phase I2014

All phases

2016

2017

2018

Source: Deloitte LLP, 201923

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 15. Clinical trial cycle time by therapy area, 2016-19– original and extension cohorts (combined)
12.0

10.0

8.0
Total cycle time (Years)“In a bid toaccelerate drug
development
and approvals,
regional andlocal life sciences
regulators haveintroduced
a number of
initiatives toimprove cycle
times.”

6.0

4.0

2.0

0
Oncology
2016

2017

CNS
2018

Infectous disease

Metabolicdisorders

CV

2019

Source: Deloitte LLP, 2019In a bid to accelerate drug development
and approvals, regional and local life
sciences regulators have introduced anumber of initiatives to improve cycle
times, including:• in 2012, the FDA created thebreakthrough therapy designation,
allowing for accelerated approvals
for therapies that are deemed tooffer ‘substantial improvements’ overexisting therapies, having expedited
development and review36• the FDA also established the Drug
Development Tools Qualification
Program in 2012 to evaluate methods,
materials, or measures that have thepotential to facilitate drug development,
allowing for qualification of validated
animal models, biomarkers, and clinicaloutcomes assessment tools37

24• the 21st Century Cures Act, passed in2016, further enables faster approvals
by establishing a breakthroughdesignation for CAR-T cell therapies,
and more flexible approaches to clinical
trials, such as adaptive trial design andthe use of RWE for label expansion38• China has streamlined the approval
process for new drugs, createdprocedures for expedited review oforphan drugs and has begun accepting
data from foreign clinical trials in filings39• in 2016, the EMA launched the priority
medicines (PRIME) scheme, which
provides early and enhanced support
for the development of medicines thattarget unmet clinical need. In the firsttwo years, 36 drug candidates acrossmultiple therapy areas qualified foraccelerated access.40

Despite such regulatory initiatives,
clinical trial cycle times have continued
to grow. Figure 16 shows that, althougha growing percentage of the indications
pursued by the original cohort received
special designations, average cycle times
continue to increase. Adoption of theseapproaches for almost a third of all
pursued indications has not done enoughto move the lever on cycle time overall,suggesting other, more fundamental
approaches need to be consideredwhen looking to alter this part of the
productivity equation.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 16. Clinical cycle time vs percentage of pipeline sales with special
designations, 2014-19 – original and extension cohorts (combined)
6.7

35%

6.6

30%

6.57

6.58

6.61

6.64
32%

6.5 25%

25%

23%
20%

6.39
6.3

15%
6.2

6.1

10%

6.15

5%

6.0

5.9

% Special designation

Clinical cycle time (years)

23%
6.4

30%

29%

Over the past few years, many companies
inside and outside our cohort haveadopted a number of approaches,
including using study-level processoptimisation techniques such as
expedited contracting, improvementsin the clinical trial management systems(CTMS) and risk-based monitoring.
Some have begun to incorporate
digital technologies, including artificial
intelligence (AI) in the clinical trial process
to expedite patient selection andenrolment, optimise protocol design,
support site selection and capture patientreported outcomes or digital biomarkers(Figure 17).41 Some companies areincorporating patient input into protocol
design to reduce time-consuming
protocol amendments and improveenrolment and retention rates. These
approaches are relatively recent and have
not yet impacted cycle times or the costof development.

0%
2014

2015

2016

2017

2018

2019

Proportion of non-risk adjusted, total sales from special designation
Clinical cycle time

Source: Deloitte LLP, 2019

“Some companies are incorporating
patient input into protocol design
to reduce time-consuming protocol
amendments and improve enrolment
and retention rates.”

25

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 17. Digital approaches to expedite clinical trials

Use AI to analyse andinterpret unstructured
data from previous studies
and scientific literature
to optimise protocols

Mine patient healthrecords to match
patients to trials

Analyse historical
data on patient locationand site activity to help
inform study feasibility
and study start-up
decisions

Automate
processes such as data
capture, drafting site andinvestigator contracts,and digitalising standard
clinical assessments toimprove investigatorproductivity

Use AI tools to ensure
medication ingestion,
identify missed clinicalvisits, and trigger non
adherence alerts

Use wearables andsensors to collect digital
endpoints on diseaseprogression and qualityof life indicators

Use natural language
processing (NLP) toautomate creation ofsafety and efficacy
sections of study
dossiers and enable
faster filing

Source: Deloitte LLP, 2019

At present, companies are just beginningto explore the potential of AI for drug
development. AI technologies canhelp improve filing efficiency, optimise
the generation of suitable amounts
of appropriate endpoints matched toregulatory requirements, and help thedesign, execution and administration ofclinical studies. For instance, machinelearning (ML) algorithms can proactively
mine electronic health records (EHRs)
and patient and clinical trial databases to
identify potential matches between trialsof relevance and specific patients, digitallyenrolling patients and saving time on
recruitment.42 A few start-ups have alreadybuilt systems using ML for automatedtrial matching (Case study 4).
26Case study 4
Machine learning to enable patients to findthe right trial
Antidote, through its platform, Antidote Match™, mines data from www.
clinicaltrials.gov and uses ML along with minor human intervention to create
structured eligibility criteria for single or multiple studies. The platform
automatically generates a pre-feasibility questionnaire that translates
complicated medical terms into easy-to-understand language for patients.
Filling in the questionnaire enables Antidote Match™ to provide patients witha list of potential accessible trials that they are eligible for. Having completed
over 100 recruitment projects, Antidote Match™ is the first clinical trial
matching engine that uses structured eligibility criteria and AI algorithms to
explore a patient’s eligibility for one or many trials, thereby accelerating therecruitment process.43

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 18. Transformative approaches have the potential
to reduce cycle time significantly• Study-level process optimisation(e.g., risk-based monitoring, expediting
contracting)

Low
Transformative Approaches

Cycle time reduction

Incremental Approaches• Historical data use in feasibility & start-up• Synthetic control arms

• Use of real-world data to identify andenroll patients• Master protocols• Robotic process automation• Label expansion through RWE• At-risk dossier creation• Identifying and cultivating responsive
subpopulations• Novel surrogate endpoints• Trial simulations or use of natural history
studies to inform protocol design

High

Source: Deloitte LLP, 2019

Increasing availability of disease specificresearch datasets, clinical genomics dataand other real-world data (RWD), coupledwith advanced analytics and AI, are aiming
to power transformative approaches forcycle time reduction (Figure 18). These
approaches can help companies makebetter decisions on product profiles,
identify high-responder sub-populations,
simulate clinical trial designs and support
regulatory submissions. The use of natural
history studies (information about thenatural trajectory of a disease, in theabsence of an intervention, from onset
until either its resolution or the individual’s
death) in rare disease, synthetic control
arms and collection of post-market realworld clinical data could reduce the needfor some aspects of the clinical trial. A few
companies are already:• expanding label indications for onmarket drugs without conducting trials• building synthetic control arms from
historical clinical data to reduce
recruitment time and effort• using novel surrogate endpoints foraccelerated approval• leveraging adaptive trials designs toallow quick and simultaneous evaluationof new drugs for multiple indications(Case study 5 and Case study 6).
As explored in our November 2019 reportIntelligent drug discovery: Powered by AI,
AI will be also be an important acceleratorof drug discovery and early stage
development. AI technologies applied tohistorical trial data can investigate thepotential relevance of already trialled
drugs against comorbidities for drug

repurposing. Combining AI with othertechnologies such as organ-on-a-chip,
3D cell cultures and other cell models
that replicate human biology in vitro
could eventually eliminate preclinicaltesting. Furthermore, advanced computermodelling and simulations that test thesafety of compounds (in silico trials) could
eliminate the need for Phase I trials in
healthy volunteers.44

Case study 5
Adaptive trial design to expedite time to market
Merck adopted a unique development pathway for Keytruda® (animmunotherapeutic), whose new drug application to initial approval took only
four years, a radical reduction in development timelines. Using an adaptive
trial design, Merck tested Keytruda®’s tolerability and impact on advanced solid
tumours. It then added in melanoma and non-small cell lung cancer (NSCLC)specific expansion cohorts for dose finding and efficacy assessments. Over theyears, Keytruda® has been approved for several additional indications, including
other forms of NSLC, melanoma and classical Hodgkin lymphoma.45

27

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 19. Approved indications for Keytruda® use in the US

2010

2014

Investigational
drug application
to FDA for
advanced solid
tumorsFirst accelerated
approval

2012

2016

Orphan drug
designation
for advanced
melanoma

2 additional
approvals

2013

2011

Breakthroughtherapy
designation
for advanced
melanomaFirst inhuman

Development pathway:
Nested Phase II study with 4 years from application
to approval for 2 indications

2017
2 additional
approvals

2015
2 additional
approvals

Illustrative timeline. Not inclusive of all
development and approval activity

Source: Deloitte LLP, 2019

Case study 6
Basket trial design leads to approval forfirst-in-class treatment for a rare cancer
Roche ran a tumour histology independent Phase II ‘basket trial’ toinvestigate Zelboraf® (vemurafenib) against BRAF–mutated cancers. These
included colorectal cancer, multiple myeloma, Erdheim-Chester disease(caused due to abnormal multiplication of white blood cells) and others. The
design involved seven patient cohorts to simultaneously evaluate efficacy
of Zelboraf® either as a standalone or a combination therapy. Study results
led to FDA approval for Zelboraf® as a breakthrough therapy for ErdheimChester disease in November 2017.46

28

Deloitte’s view
Reducing cycle times is critical forbiopharma companies. Continued
collaboration with regulators,
optimisation of study-level processes
using digital technologies and othertransformative approaches aimed at
helping biopharma companies reduce
cycle times may soon come to fruition.
Oncology is one therapy area wherethese approaches are starting to be
applied extensively. The impact ofapproaches such as automating data
capture, drafting site and investigator
contracts and digitalising standard
clinical assessments on reducing cycle
times will be evident in the next twoto three years. This, coupled withnew regulatory frameworks such as
the FDA’s Real-Time Oncology Review(RTOR), will enable data-driven R&D
processes to speed up approvals andcycle times.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Notably, the original cohort is increasingly
reliant on M&A as a source of innovation.
This may be indicative of the challenges
larger companies face in achieving growth
on top of an already sizable revenue base,prompting them to seek consolidation tobolster pipelines and improve productivity
through synergies. In contrast, theextension cohort is increasingly relying
on in-licensing and co-development,suggesting more specialised companies
are partnering to access capability as wellas innovation.
The past few years have also seen the
return of ‘mega-mergers’, highlighting
the movement towards consolidation
within the industry. There have also been
a number of notable partnering deals,
illustrating the high price companies areprepared to pay to access innovative
pipeline products. This places emphasison the need to extract value from these
deals, which given the complexity andchallenges involved, is not always
straightforward.

Figure 20. Proportion of late-stage pipeline sourced from internal and externalsources, 2010-19 – original and extension cohorts
100%
90%
80%
70%
60%
Percentage of forecast late-stage pipeline revenue based on non-risk adjusted total inflows (%)

Sources of innovation for biopharma
are changing
In 2010, close to half of the original andextension cohorts’ late-stage pipelines
were sourced through external innovation.
This trend has fluctuated, with increasing
and decreasing reliance on externalinnovation over the last 10 years. However,in the in the last two years, greater than
50 per cent of both cohorts’ late-stage
pipelines have been sourced externally.47
Prior research suggests that externallysourced innovation launches at higher
rates than industry benchmark, making
this a desirable approach (Figure 20).

50%
40%
30%
20%
10%
0%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2013

2014

2015

2016

2017

2018

2019

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Report year
In-licenced

Joint venture

Acquired

Self-originatedCo-developed

Source: Deloitte LLP, 201929

Ten years on | Measuring the return from pharmaceutical innovation 2019

Figure 21. Number of NME/NAS approvals, 2011-19 – original and extension
cohorts (combined)
60

50

Number of NME/NAS

Moreover, an increasing proportion of newmolecular entity/new active substance(NME/NAS) approvals have come fromoutside the cohort over the last 10 years(Figure 21). This suggests that novel
drug approvals, which are more likely
to command higher market share andpricing, are increasingly coming fromsmaller or newer start-up companies.
These companies are increasingly lessreliant on big pharma capabilities andcapital to shepherd potential drugcandidates through the drug development
process. This trend raises questions
around the sustainability of big pharma’s
current innovation model, and whether
smaller companies may ultimately takean increasing share of the market bydeveloping and commercialising products
independently. Some smaller players
have already seen success in the market(Case study 7 and Case study 8).

40

30

20

10

0

FDA EMAFDA EMAFDA EMAFDA EMAFDA EMAFDA EMAFDA EMAFDA EMAFDA EMA

2011

2012

2013

2014

2015

2016

2017

2018

2019

Outside of our cohort

From our cohort

Projected

NAS = New Active Substance, approved by the EMA. NME = New Molecular Entity, approved by the FDA.
EMA date is based on CHMP opinions

Source: Deloitte LLP, 2019

Case study 7
Bringing the first RNAitherapeutic to market
In 2018, the FDA granted approval to Alnylam
Pharmaceuticals to market the first RNAi (RNAinterference) therapeutic, ONPATTRO™ (patisiran), for
ATTR amyloidosis, a disease that leads to a build-up of
toxic proteins in the kidney. While most drugs counteract
the effects of harmful proteins, RNAi therapeutics
disrupt mRNA (messenger RNA) by silencing the genesthat transcribe harmful proteins. Since the discoveryof RNAi in 1998, which led to the 2006 Nobel Prizein Medicine, several large biopharma companies
attempted but failed to bring such RNA therapies tomarket. Despite several technological and financial
roadblocks, Alnylam Pharmaceuticals was the firstcompany to successfully develop and commercialise an
RNAi therapeutic in the US.48,49

30

Case study 8
Launching the first oral
medication for Fabry’s disease
In 2019, Amicus Therapeutics received FDA approval tolaunch the first oral medication Galafold™ (migalastat) totreat adults with Fabry’s disease. This disease occurs due
to genetic mutations leading to an enzyme deficiency,
which results in the accumulation of a harmful type offat called globotriaosylceramide (GL-3) in various organs
and tissues. Traditional treatment of Fabry’s disease
involves enzyme replacement therapy. Galafold™, on
the other hand, binds to and stabilises dysfunctional
enzymes, clearing the accumulated GL-3 from thebody.50

Ten years on| Measuring the return from pharmaceutical innovation 2019

This trend is unlikely to change, sincecompanies from outside our cohorts aresponsoring an increasing proportion ofclinical trials. In 2010, our original andextension cohort companies sponsored
56 per cent of all trials, which decreased
to 43 per cent by 2019. Also, there has
been sharp decrease (from 58 per centin 2010 to 42 per cent in 2019) in thepercentage of Phase I trials sponsoredby our original and extension cohort
companies (Figure 22).

Running clinical trials has traditionallyrequired a significant amount of capitaland scale, and smaller companies haverelied on bigger biopharma companies
as partners to provide these resources
and capabilities. Furthermore, developing
drugs that treat chronic disease in large
populations required large, multi-siteand multi-year trials. Today, the shift in
focus towards new modalities targetingsmaller populations, together with an
influx of capital into the biotech market
and increasing capabilities from theClinical Research Organisation (CRO)industry, have enabled smaller companies
to be able to sponsor clinical trials
independently.

The shift in focus towards new modalities
in disease areas with high unmet needhas also changed the nature of clinicaldevelopment programmes. Smaller
companies focusing on disease areas,
like rare and orphan diseases, are moreagile and can pursue smaller patient
populations or accelerated pathways.
According to IQVIA, ‘emerging biopharmacompanies, active in the fastest growing
areas of oncology and orphan drugs
accounted for 72 per cent of the 2018
late-stage pipeline activity, up from 61
per cent a decade ago.’51 Strong capital
markets and smaller scale clinical trials
have likely contributed to the reduced
need for these companies to partner or
be acquired to develop their therapies.

Figure 22. Cohort vs non-cohort company sponsorship of clinical trials, 2010-19– original and extension cohorts (combined)

Deloitte’s view60%

by companies within our cohort

Percentage of interventional PhI-III trials sponsored

65%

55%

50%

45%

40%

35%

30%
2010

2011

2012

2013

2014

2015

Report year
Phase I

Phase II

Source: Deloitte LLP, 2019

Phase IIIAll phases

2016

2017

2018

2019

In recent years, our cohorts haveshifted to buying or partnering toaccess innovation. This reflects thegrowing strength of entrepreneurial
scientists, leading clinical research
centres and visionary smaller
companies as key originators ofscientific innovation. In building
new R&D models, biopharma
has sought to replicate these
innovation principles, but also
added acquisition and partnerships
to accelerate drug discovery.
External innovation will therefore
continue to be an important part oflarge biopharma companies’ R&D
strategies. However, this places
additional emphasis on integratingand extracting the full value fromthe organisations being acquired.
If they are not a sound strategicand operational fit, this can create
barriers to realising the deal andoverall R&D returns.

31Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 23. Value and volume of PE and VC investments in biotechnology
companies, 2010-18
300

10
9

250

230

Number of deals

204

199

7
6

249
235

191

200

184
162

5

150

4
100

3
2

50

1
0

0
2010
Value (USD $bn)

2011

2012

2013

2014

2015

No of deals

Source: Deloitte analysis of Capital IQ, as of 11th November 2019
*Please note this data includes VC and PE investments in the following: biological products, gene researchand development, pharmaceutical products, protein and genome sequence products and r-DNA
pharmaceuticals

Capital investment in biotech startups and clinical trial outsourcing isincreasing
Since 2017, there has been a substantial
increase in the total value of biotech
deals, despite the number of deals staying
relatively flat (Figure 23). Notably, partof the increase in private equity (PE) andventure capital (VC) investment went tocompanies focused on new modalities(Figure 24).

32

Over time, the size of the clinical trial
outsourcing market has increased. There
has been a consolidation of players inthis space, with growing infrastructure
and advancing capabilities. While lacking
the infrastructure to conduct trials
themselves, emerging companies may
be channelling funds they have received
into accessing CRO services.52 In the past
12 to 18 months, a number of large CROs
have launched new service lines targeting
emerging biotech and biopharmacompanies.53,54

2016

2017

2018

Deal value (USD $billion)

8247

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 24. Increase in value of PE and VC investments in select areas
2%

7%

9%
14%

8%

2016

81%

2018

9%

53%

17%

Pharmaceutical products

Gene research and development

Protein and genome sequence products

Biological products

Recombinant DNA pharmaceuticals

Source: Deloitte analysis of Capital IQ, as of 11th November 2019

Deloitte’s view
Pharma are not the only investors seeking returns
from early innovation – the rise in PE and VC
investment in biotech companies indicates the value
biotech companies are creating around cuttingedge innovations in ‘next gen’ therapies. At thesame time, outsourcing companies such as CROs
and professional service firms are starting to build
services to provide support to new biotech andbiopharma companies to progress the development
and even commercialisation of new drugs. This
makes new biotech and biopharma companies
less reliant on larger biopharma partners tocommercialise therapies. Biopharma companies that
seek to partner or acquire companies focusing on‘next gen’ therapies are likely to face stiff competitionand high valuations, further negatively impacting
their returns on R&D. Some biopharma companies
are increasingly turning to academic partnerships toaccess innovation earlier in the value chain and focus
their internal capabilities. This focus of capabilities
also relates to other collaborations, where the ability
to bring together different skill sets helps to improvethe chances of the partnership’s success.

“Biopharma companies
that seek to partner
or acquire companies
focusing on ‘next gen’
therapies are likely toface stiff competitionand high valuations.”

33

Ten years on| Measuring the return from pharmaceutical innovation 2019

Shaping the futureof biopharma innovation
An increasing amount of capital investment in biotech is driving higher asset andcompany valuations, and allowing emerging companies to pursue development
into later stages. Will large cap biopharma companies be able to continue to buy
innovation? Our cohort companies are likely to face threats from emerging biopharmacompanies focused on both scientific and digital innovation. In a future of healthdriven by shared, radically interoperable data, empowered consumers and scientificbreakthroughs, biopharma companies will need to develop core capabilities thatare entirely different from today.55 These capabilities are likely to be focused aroundthe need to access, analyse and interpret large datasets, including deploying
AI technologies. This will require a fundamental shift in the biopharma R&D model.
The impact of digital transformation
on R&D, including the rise of AI for
drug discovery companies
Digital technologies are starting totransform how biopharma companies
approach clinical development byincorporating insights from multiple
sources of data, providing an opportunity
to improve the patient experience,enhance clinical trial productivity,and increase the amount and qualityof data collected in trials. However,digital transformation is not just about
technologies, platforms and advanced
analytics; it is a way of doing things
differently. Consequently, adopting a digital
mind-set is a new business imperative.
A comprehensive digital R&D strategy
can be critical to enable companies tomove and process large amounts ofdata effectively, to make data-driven
scientific and business decisions
quickly and accurately, and to generate
evidence in support of future productvalue propositions. This will require
new capabilities, new skill sets and new
partnerships.56

34

Deloitte’s recent report Intelligent drug
discovery: Powered by AI identified
significant growth in the AI for drug
discovery landscape, which comprised
170 AI companies, 50 corporations, 400
investors and 35 major R&D centres in July
2019. The market for AI in drug discovery
increased from $200 million in 2016 to$700 million in 2018 and is expected toreach $20 billion in the next five years.
The report highlights the fact that AIdriven drug discovery companies are
utilising vast amounts of biopharma
research data, including RWD, to move
more targeted new drug candidates into
clinical trials, in some cases in months
rather than years.57,58

Some AI for drug discovery companies
are building their own pipeline ofproducts and seeking to bring products
to market independently. This will become
increasingly possible with an influx of AIdriven drug development capabilities, bothwithin CROs, as well as from start-ups.
Indeed, most large biopharma, including
our cohort companies, have started topartner with AI start-ups to support drug
development. We conclude that thesecompanies will be better prepared tocompete in the future and those that do
not pursue these capabilities are likely tobe left behind. Moreover, AI solutions, if
adopted at the drug-discovery stage, havethe potential to kick-start the productivityof the entire R&D process.59

Deloitte’s view
AI is only one strand of innovation that companies are pursuing as part of theirdigital transformation of R&D. Digital processing of large genetic, phenotype
and medical data creates the potential for computational drug discovery
and development, with new analytical models and AI improving cycle time
and technical chance of success. Other strands of innovation include earlier
partnering, new operating models to support cell and gene therapies,
digital therapeutics and interventions focused on prevention and disease
modification rather than symptomatic management or acute intervention.
Adopting these approaches will be an imperative for biopharma companies.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 25. The three main business archetypes that are likely to apply to biopharma in the Future of Health

Data Convener
Aggregate/store individual,
population, institutional, environmental data
(e.g. EHRs). Enable interoperability and ensure
privacy security

Data collectors

Data connectors

Data securers

Engine developers

Analytics gurus

Insight discoverers

Science and Insights Engine
Conduct research and generate data insights far
beyond human capabilities to aid care delivery(e.g. personalised therapy, drug discovery,
wellness coaching, clinical decision support)

Data and Platform
Infrastructure Builder
Develop and manage site-less healthinfrastructure (e.g. app store-like platform);
set standards for platform components

Core platform developers

Platform managers
and operators

Source: Deloitte analysis

The ‘Future of Health’ and its
implications for biopharma
As innovations in medical technology
enable us to diagnose genetic andgenomic–based diseases much earlier
and more precisely, there will be a shift
to curative therapies, targeted at muchsmaller populations. For the rest of the
population, there will more customised
medicines, driven by the availability oflarge, real-world datasets informing
subpopulations of high- and lowresponders. The majority of treatments
will be combinations of generics andbiosimilars, using customised doses thatdrastically improve patient outcomes.
There will also be a much greater use of
non-pharmacological interventions, suchas digital therapeutics and microbiomebased therapies for treating or preventing
conditions such as obesity, pain,
depression and cognitive decline.60

The past 10 years of decline in IRR
illustrates quite starkly that thebiopharma industry needs new models
of R&D. Furthermore, revenue-impacting
disruptions from technology, smaller
biopharma and biotech companies are
happening right now, requiring an urgent
response. This, together with the changes
brought by the future of health, meansthat biopharma companies will have tothink radically differently about what typeof company they want to be, and where
and how they want to play. The growing
number of rich datasets, and advancesin genomics, analytics and science moregenerally, will require every biopharmacompany to make hard decisions onwhat type of R&D model will be mostappropriate for their future sustainability.

For health care more generally, Deloitte
has identified 10 different data andinformation archetypes that will define
the future of health. For biopharma, we
believe that the most likely archetypes
are a combination of data conveners,science and insight engines, and data andplatform infrastructure builders. The key
questions for biopharma are whether
to specialise or integrate the different
archetypes, and what this means fortheir R&D model, especially around thescience and insights engine. Furthermore,
can biopharma use these archetypes
to increase their returns and theaffordability of medicines (see Figure 25).

35

Ten years on| Measuring the return from pharmaceutical innovation 2019

Regardless of the archetypes that eachcompany pursues, there are fundamental
capability investments associatedwith new R&D models that need to be
adopted aggressively to succeed in thisnew environment. Many companies havebegun to make significant investments
and develop partnerships to acquire theskills and talent that are needed, including
highly trained, specialist data experts andexperts in AI and computational biology.
The specific implications of the Future of
Health for biopharma R&D are as follows:• R&D data-driven insights will enable
companies to develop customised
treatments by identifying which
therapies will work in which patients.
For example, in silico analysis will predict
drug efficacy using data available on
chemical drug features and genomic
make-up. Synthetic trials with near
perfect information and the applicationof advanced analytics will shift drug
discovery from bench to in silico• advanced analytics (ML, NLP, etc.)
and robotic process automation will
enable end-to-end automation of R&D,
reducing timelines significantly. This will
also shift the traditional cost of drug
development from large-scale clinical
trials to the storage and computationof large datasets to develop highly
effective personalised therapies

36• R&D costs will shift from traditional
discovery and trial execution (requiringextensive amounts of clinician
involvement and challenges in patient
recruitment and retention) to a process
driven by large RWE health datasets,
investments in interoperability, andadvanced computing power and clouddata storage• sophisticated drug development
algorithms will inform personalised,active pharmaceutical ingredient (API)combinations that can be manufactured
and delivered directly to the consumer
by pharmacies with, for example, 3D
printing customising APIs to deliver
personalised therapies• scientific breakthroughs, includingstem cells, nanobots, biome sensors,and others, will occur at an exponential
pace, building on the insights derived
from radically interoperable data,with industry and new incumbents
disrupting the market and transforming
clinical trial structures, timing, including
increased participation from the ’crowd’• as drug targets shift from symptom
management and disease modification
to curative and preventative
approaches, mass-market maintenancetherapies will decline. For example,
DNA-based insulin gene therapy
will likely eliminate Type 1 Diabetes,
and hyper-tailored therapies, earlyintervention and enhanced adherence
should lead to a drop in volume of units
and increases in prices.61

Deloitte’s view
While we maintain the ‘tempered
optimism’ from our first Measuring
the return from pharmaceuticalinnovation report aboutbiopharma’s future and thatbiopharma companies can reverse
the past decade of decline shown in
our analysis, this will require radical
decisions about future business andoperating models. We believe that
advances in science and technology
will find solutions to the mostpressing unmet needs of patients.
However, the challenges around
reducing R&D costs, declining
expected peak sales, expanding
regulatory requirements andmore demanding reimbursement
hurdles will remain. With data andinformation the life-blood thatsustains R&D, we envisage somebiopharma companies will become
data organisations, while others
will transition to a leaner, more
focused, science-based modelaligned to innovation clusters anda growing revenue stream from
speciality products and biologics.
Data, technologies and new science
will create new discoveries, curesand customised medicines, brought
to market by changing regulatoryenvironments and greater R&D
precision. The future is better than
today. We just do not know it yet.

Ten years on| Measuring the return from pharmaceutical innovation 2019

Appendix37

20
1
E Ne 0Ap xist w
Te pr ingor
R& min vedaD
co Ma ted
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ur
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Ap is w
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ur
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Ex Ne 4
Ap is w
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ur
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ur
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Ex Ne 6
Ap is w
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co Ma ted
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ur
O e)
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20 r
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Ex Ne 7
Ap is w
Te pr tingr o
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ur
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th
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20 r
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co Ma ted
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(p in
ur
O e)
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e
20 r
19

38

2
6.7%
9.3%

4.7%
4.0%
3.3%

4

Source: Deloitte LLP, 2019

Due to rounding, numbers presented throughout this document may not add up
precisely to the totals provided, and percentages may not precisely reflect theabsolute figures.
2015-172.5%-5%
2016-18

Original cohort

14.5%
13.1%

+2.7
+0.7
-2.3

6.0%

+1.1
-1.5
-0.6
+0.2
-0.7
-0.1
3.7
+1.6
+0.7
-2.7
-0.7
+0.1
-0.9

2014-16+1.0

2013-15

-0.3
-0.1
-0.3
+0.6
4.2

5%

+3.2
0.0
-3.0

5.0%

+2.0
+0.5
-2.7

2012-14

+3.2
-0.1
-1.6

$17.0%

-0.8
+0.2
-0.7
0.0
1.8

0.0
1.9

6
2011-13

-0.7
+0.2
-0.5
-1.1

2010-12

+2.3
-0.2
-2.5

+3.2
-1.2

8.4%

-0.9
+0.4
-0.5
+0.5
7.3

-1.8

+1.3
-0.7

12
-2.7

15%

-0.2
-0.2
-0.5
-0.2
4.2

8

-0.6
+0.5
+0.6
-0.8
7.6

10
10.1

IRR (%)
10%

-0.7
-0.2
-0.1
+0.4
5.5

4.8

Absolute IRR (%)

Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 26. Three-year rolling average returns on late-stage pipeline, 2010-19– original and extension cohorts

20%
Extension cohort

Top performer

10.9%
Third quartile
Mean average

MedianFirst quartile0%
Bottom performer

2017-19
Original cohort

Source: Deloitte LLP, 2019

Figure 27. Year-on-year drivers of change in IRR, 2010-19 – original cohort

140Ten years on| Measuring the return from pharmaceutical innovation 2019

Figure 28. Three-year rolling average R&D cost to develop an asset from discovery to launch, 2010-19– original and extension cohorts

4,000
Extension cohort
Top performer$2,440$2,2632,000$1,277$1,222$1,442$1,306$1,472$1,587$1,9711,000$1,309$1,626$1,793

Original cohort

Costs to launch ($m)

3,000

Third quartile
Mean average
MedianFirst quartile$1,082

Bottom performer
Original cohort0
2010-122011-132012-142013-15

2014-16

2015-172016-18

2017-19

Source: Deloitte LLP, 2019

Figure 29. Three-year rolling average peak sales per pipeline asset, 2010-19 – original and extension cohorts$1,200Extension cohort$1000$1,037$1,083$1,095$985

Original cohort$800
Peak sales ($m)

Top performer$1,027

$600
$692
$571

$400

Third quartile
Mean average
MedianFirst quartile

$497
$451

$200

$427

$418

$416
$408

Bottom performer
Original cohort$0
2010-122011-132012-142013-15

2014-16

2015-172016-18

2017-19

Source: Deloitte LLP, 201939

Ten years on| Measuring the return from pharmaceutical innovation 2019

Endnotes
1.

R&D value measurement: Is R&D earning its investment? Deloitte, 2010.

2.

Unlocking R&D productivity: Measuring the return from pharmaceutical innovation 2018. Deloitte LLP, 2018. See also: https://www2.deloitte.com/uk/en/
pages/life-sciences-and-healthcare/articles/measuring-return-from-pharmaceutical-innovation.html

3.

Dr Wayne Bowen, New modes of drug action tackle intractable diseases, Medium, 25 January 2019. See also: https://medium.com/@ttp_plc/new-modes-ofdrug-action-tackle-intractable-diseases-e7d8e2c31759

4.

Alexander Denoon and Erik Vollebregt, Can regenerative medicine save Big Pharma’s business model from the patent cliff?, Future Medicine, 27 September
2010. See also: https://www.futuremedicine.com/doi/10.2217/rme.10.68?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dwww.
ncbi.nlm.nih.gov&

5.

Generic Drugs: Questions & Answers, U.S. Food and Drug Administration (FDA), 01 June 2018. See also: https://www.fda.gov/drugs/questions-answers/
generic-drugs-questions-answers

6.

The future of the life sciences industries: Aftermath of the global recession, Deloitte Touche Tohmatsu, 2010. See also: https://www2.deloitte.com/content/
dam/Deloitte/de/Documents/life-sciences-health-care/lshc_DE_Future_of_Life_Science_industry_2010.pdf

7.

2018 Global life sciences outlook: Innovating life sciences in the fourth industrial revolution, Deloitte, 2018. See also: https://www2.deloitte.com/content/
dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-ls-outlook-2018.pdf

8.

Christopher J. Morten and Amy Kapcynski, Assessing drug pricing reform proposals: The real leverage and benefits of competitive licensing, Health Affairs
Blog, 4 November 2019. See also: https://www.healthaffairs.org/do/10.1377/hblog20191101.594551/full/

9.

Andrii Buvailo, Will Biologics Surpass Small Molecules In The Pharma Race?, Biopharma Trend, 11 July 2018. See also: https://www.biopharmatrend.com/
post/67-will-small-molecules-sustain-pharmaceutical-race-with-biologics/

10. Novel Drug Approvals for 2019, U.S. Food and Drug Administration (FDA), November 2019. See also: https://www.fda.gov/drugs/new-drugs-fda-cders-newmolecular-entities-and-new-therapeutic-biological-products/novel-drug-approvals-2019
11. Samantha DiGrande, Second Patent Cliff Lies Ahead for Pharma With $251 Billion in Sales at Risk by 2024, The Center for Biosimilars, 07 June 2018. See also:
https://www.centerforbiosimilars.com/news/second-patent-cliff-lies-ahead-for-pharma-with-251-billion-in-sales-at-risk-by-2024
12. Erwin A. Blackstone and P. Fuhr Joseph, The Economics of Biosimilars, National Center for Biotechnology Information, September 2013. See also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4031732/
13. Thomas Croisier and James Forsyth, As biosimilars enter the US market, will they be used, and can they reduce spending?, Deloitte LLP, March 2018. See also:
https://blogs.deloitte.co.uk/health/2018/03/as-biosimilars-enter-the-us-market-will-they-be-used-and-can-they-reduce-spending.html
14. Karen Taylor and Pratik Avhad, The cost-effectiveness of biosimilars: Delivering value for money from pharma spending, Deloitte LLP, September 2019. See
also: https://blogs.deloitte.co.uk/health/2019/09/the-cost-effectiveness-of-biosimilars-delivering-value-for-money-from-pharma-spending.html
15. Thomas morrow and Linda H. Felcone, Defining the difference: What Makes Biologics Unique, National Center for Biotechnology Information, September
2004. See also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3564302/
16. Mathias Cousin et al., Demonstrating Value And Securing Patient Access For Cell And Gene Therapies, Cell & Gene, 02 May 2019. See also: https://www.
cellandgene.com/doc/demonstrating-value-and-securing-patient-access-for-cell-and-gene-therapies-0001
17. Mathias Cousin et al., Demonstrating Value And Securing Patient Access For Cell And Gene Therapies, Cell & Gene, 02 May 2019. See also: https://www.
cellandgene.com/doc/demonstrating-value-and-securing-patient-access-for-cell-and-gene-therapies-0001
18. Sanjay Srivastava et al., Transforming Next-Gen Therapy Supply Chains Into Patient-Connected Value Chains, Cell & Gene, 20 June 2019. See also: https://www.cellandgene.com/doc/transforming-next-gen-therapy-supply-chains-into-patient-connected-value-chains-0001
19. Mathias Cousin et al., Demonstrating Value And Securing Patient Access For Cell And Gene Therapies, Cell & Gene, 02 May 2019. See also: https://www.
cellandgene.com/doc/demonstrating-value-and-securing-patient-access-for-cell-and-gene-therapies-0001
20. Sanjay Srivastava et al., Establishing And Maintaining A Treatment Network For Your Cell And Gene Therapy Product, Cell & Gene, 30 July 2019. See also:
https://www.cellandgene.com/doc/establishing-and-maintaining-a-treatment-network-for-your-cell-and-gene-therapy-product-0001
21. A new future for R&D?: Measuring the return from pharmaceutical innovation 2017, Deloitte LLP, 2017. See also: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/deloitte-uk-measuring-roi-pharma.pdf
22. CAR-T, U.S. National Library of Medicine: Clinical Trials, December 2019. See also: https://clinicaltrials.gov/ct2/results?cond=&term=&type=&rslt=&age_
v=&gndr=&intr=CAR-T&titles=&outc=&spons=&lead=&id=&cntry=&state=&city=&dist=&locn=&strd_s=&strd_e=&prcd_s=&prcd_e=&sfpd_s=&sfpd_
e=&lupd_s=&lupd_e=&sort=40Ten years on| Measuring the return from pharmaceutical innovation 201923. Global B-cell NHL market set to reach $9.2bn by 2027, Global Data, 15 July 2019. See also: https://www.globaldata.com/global-b-cell-nhl-market-set-to-reach9-2bn-by-2027/
24. Final evaluation document: Voretigene neparvovec for treating inheritedretinal dystrophies caused by RPE65 gene mutations, National Institute for Healthand Care Excellence (NICE), August 2019. See also: https://www.nice.org.uk/guidance/hst11/documents/final-evaluation-determination-document-2
25. NHS to fund revolutionary treatment for blindness in children, NHS England, 04 September 2019. See also: https://www.england.nhs.uk/2019/09/nhs-tofund-revolutionary-treatment-for-blindness-in-children/
26. Ibid.
27. Zynteglo, European Medicines Agency (EMA). See also: https://www.ema.europa.eu/en/medicines/human/EPAR/zynteglo
28. bluebird bio Announces EU Conditional Marketing Authorization for ZYNTEGLO™ (autologous CD34+ cells encoding βA-T87Q-globin gene) Gene Therapy
for Patients 12 Years and Older with Transfusion-Dependent β-Thalassemia Who Do Not Have β0/β0 Genotype, bluebird bio, 2019. See also: http://investor.
bluebirdbio.com/news-releases/news-release-details/bluebird-bio-announces-eu-conditional-marketing-authorization
29. Ibid.
30. Jinkuk Kim et al., Patient-Customized Oligonucleotide Therapy for a Rare Genetic Disease, The New England Journal of Medicine, 24 October 2019. See also:
https://www.nejm.org/doi/full/10.1056/NEJMoa1813279
31. Marie Powers, Tufts: Clinical trials face tough going as data densities grow, BioWorld, 18 July 2018. See also: https://www.bioworld.com/articles/368205-tuftsclinical-trials-face-tough-going-as-data-densities-grow?v=preview
32. Stefan Harrer et al., Artificial Intelligence for Clinical Trial Design, Cell Press, August 2019. See also: https://www.cell.com/action/
showPdf?pii=S0165-6147%2819%2930130-0
33. Ibid
34. Marie Powers, Tufts: Clinical trials face tough going as data densities grow, BioWorld, 18 July 2018. See also: https://www.bioworld.com/articles/368205-tuftsclinical-trials-face-tough-going-as-data-densities-grow?v=preview
35. Immuno-Oncology Clinical Trials Increased At 17% Average Yearly Growth Between 2008 And 2017, Says GlobalData, Clinical Leader, 28 February 2018. See
also: https://www.clinicalleader.com/doc/immuno-oncology-clinical-trials-increased-average-yearly-growth-between-and-says-globaldata-0001
36. Breakthrough Therapy, U.S. Food and Drug Administration (FDA), January 2018. See also: https://www.fda.gov/patients/fast-track-breakthrough-therapyaccelerated-approval-priority-review/breakthrough-therapy
37. Drug Development Tools, U.S. Food and Drug Administration (FDA), June 2019. See also: https://www.fda.gov/drugs/development-approval-process-drugs/
drug-development-tools-ddts
38. 21 Century Cures: The future of product innovation and approval, Deloitte, 2017. See also: https://www2.deloitte.com/us/en/pages/life-sciences-and-healthcare/articles/twenty-first-century-cures-act-life-sciences-development.html
39. Allie Nawrat, How government policy has shaped China’s pharma phenomenon, Pharmaceutical Technology, 25 March 2019. See also: https://www.
pharmaceutical-technology.com/features/china-pharma-success-government-policy/
40. PRIME: a two-year overview, European Medicines Agency (EMA), 2018. See also: https://www.ema.europa.eu/en/documents/report/prime-two-year-overview_en.pdf41. Dawn Anderson et al., Digital R&D: Transforming the future of clinical development, Deloitte Insights, February 2018. See also: https://www2.deloitte.com/
us/en/insights/industry/life-sciences/digital-research-and-development-clinical-strategy.html
42. Stefan Harrer et al., Artificial Intelligence for Clinical Trial Design, Cell Press, August 2019. See also: https://www.cell.com/action/
showPdf?pii=S0165-6147%2819%2930130-0
43. Inspire and Antidote launch end-to-end clinical trial recruitment solution to accelerate medical research, GlobalNewswire, 4 January 2018. See also: https://www.globenewswire.com/news-release/2018/01/04/1283227/0/en/Inspire-and-Antidote-Launch-End-to-End-Clinical-Trial-Recruitment-Solution-toAccelerate-Medical-Research.html
44. Mark Steedman et al., Intelligent biopharma: Forging the links across the value chain, Deloitte Insights, October 2019. See also: https://www2.deloitte.com/
us/en/insights/industry/life-sciences/rise-of-artificial-intelligence-in-biopharma-industry.html
45. S. P. Kang et al., Pembrolizumab KEYNOTE-001: an adaptive study leading to accelerated approval for two indications and a companion diagnostic, Annals of
Oncology, June 2017. See also: https://academic.oup.com/annonc/article/28/6/1388/3101163
46. A Study of Vemurafenib in Participants With BRAF V600 Mutation-Positive Cancers, Clinical Trials, November 2017. See also: https://clinicaltrials.gov/ct2/
show/results/NCT01524978

41

Ten years on| Measuring the return from pharmaceutical innovation 201947. Philip Pfrang et al., External innovation: How biopharma companies are bolstering R&D pipelines through deal-making, Deloitte Insights, October 2017. See
also: https://www2.deloitte.com/us/en/insights/industry/health-care/biopharma-companies-deals-research-development.html
48. Michael Hopkin, RNAi scoops medical Nobel, Springer Nature, October 2006. See also: https://www.nature.com/news/2006/061002/full/news061002-2.html
49. Ned Pagliarulo, In first, FDA approves RNA interference drug from Alnylam, Biopharma Dive, 10 August 2018. See also: https://www.biopharmadive.com/
news/alnylam-onpattro-patisiran-approval-fda-rna-interference/529700/
50. Galafold (migalastat) Indicated for the Treatment of Fabry Disease, Clinical Trials Arena. See also: https://www.clinicaltrialsarena.com/projects/galafoldmigalastat-fabry-disease/
51. The changing landscape of research and development: Innovation, drivers of change, and evolution of clinical trial productivity, IQVIA Institute, 23 April 2019.
See also: https://www.iqvia.com/insights/the-iqvia-institute/reports/the-changing-landscape-of-research-and-development
52. Melissa Fassbender, Smaller firms driving full-service CRO work as Big Pharma rethinks its outsourcing strategy, Outsourcing-Pharma, 03 April 2018. See
also: https://www.outsourcing-pharma.com/Article/2018/04/03/CRO-market-to-grow-with-smaller-firms-driving-full-service-work-as-Big-Pharma-rethinksoutsourcing-strategies
53. Ed Miseta, Report Projects Spending & Outsourcing To Drive CRO Market Growth, Clinical Leader, 26 September 2019. See also:https://www.clinicalleader.
com/doc/report-projects-spending-outsourcing-to-drive-cro-market-growth-0001
54. Melissa Fassbender, Iqvia launches new division to support emerging biotech market, Outsourcing-Pharma, 04 February 2019. See also: https://www.
outsourcing-pharma.com/Article/2019/02/04/Iqvia-launches-new-division-to-support-emerging-biotech-market
55. Neal Batra et al., Forces of change: The future of health, Deloitte Insights, April 2019. See also: https://www2.deloitte.com/us/en/insights/industry/healthcare/forces-of-change-health-care.html
56. Dawn Anderson et al., Digital R&D: Transforming the future of clinical development, Deloitte Insights, 14 February 2018. See also: https://www2.deloitte.com/
us/en/insights/industry/life-sciences/digital-research-and-development-clinical-strategy.html
57. Francesca Properzi et al., Intelligent drug discovery: Powered by AI, Deloitte Insights, November 2019. See also: https://www2.deloitte.com/us/en/insights/
industry/life-sciences/artificial-intelligence-biopharma-intelligent-drug-discovery.html
58. AI for Advanced R&D and Drug Discovery Q2 2019, Deep Knowledge Analytics, 2019. See also: https://ai-pharma.dka.global/quarter-2-2019/59. ibid
60. Digital therapeutics: Improving patient outcomes through convergence, Deloitte, 2019. See also: https://www2.deloitte.com/us/en/pages/life-sciences-andhealth-care/articles/digital-therapeutics.html
61. Neal Batra et al., Forces of change: The future of health, Deloitte Insights, April 2019. See also: https://www2.deloitte.com/us/en/insights/industry/healthcare/forces-of-change-health-care.html

42Ten years on| Measuring the return from pharmaceutical innovation 2019

Notes

43

Ten years on| Measuring the return from pharmaceutical innovation 2019

Contacts
Authors

Mark Steedman
Manager
UK Centre for Health Solutions
Deloitte LLP+44 (0) 20 7007 8857
msteedman@deloitte.co.uk

Mark Stockbridge
Senior Manager
Life Sciences R&D Advisory
Deloitte MCS Ltd+44 (0) 20 7303 7539
mstockbridge@deloitte.co.uk

Sonal Shah
Senior Manager
US Center for Health Solutions
Deloitte Services LP+1 (212) 653 6025
sonshah@deloitte.com

Karen Taylor
Director
UK Centre for Health Solutions
Deloitte LLP+44 (0) 20 7007 3680
kartaylor@deloitte.co.uk

Maria João Cruz
Research Analyst
UK Centre for Health Solutions
Deloitte LLP+44 (0) 20 7303 4434
mariajoaocruz@deloitte.co.uk

Wendell Miranda
Senior Analyst
US Center for Health Solutions
Deloitte Services LP+1 (615) 209 6896
wmiranda@deloitte.com

Deloitte EMEA contacts

Deloitte US contacts

Global Data contacts

Julian Remnant
Partner, EMEA Life Sciences
R&D Advisory Lead
Deloitte MCS Ltd+44 (0) 20 7303 3303
jremnant@deloitte.co.uk

Sarah Thomas
Managing Director
US Center for Health Solutions
Deloitte Consulting LLP+1 (202) 220-2749
sarthomas@deloitte.com

Hanno Ronte
Partner
Life Science & Healthcare
Monitor Deloitte+44 (0) 20 7007 2540
hronte@deloitte.co.uk

Gregory Reh
Principal, Global Sector Leader
Life Sciences
Deloitte Consulting LLP+1 (215) 997 7559
grreh@deloitte.com

Colin Terry
Partner
Life Sciences R&D Advisory
Deloitte MCS Ltd+44 (0) 20 7007 0658
colterry@deloitte.co.uk

John HaugheyPartner, Global Life Sciences
Consulting Leader
Deloitte MCS Ltd+44 (0) 20 7303 7472
jhaughey@deloitte.co.uk
Mike Standing
Partner, Life Sciences and
Healthcare Leader, EMEA
Monitor Deloitte+44 (0) 20 7007 3178
mstanding@deloitte.co.uk

44

Neil Lesser
Principal,
Life Sciences R&D Practice Leader
Deloitte Consulting LLP+1 (215) 446 4364
nlesser@deloitte.com

David Abramson
Global Director of Pharma Consulting
GlobalData+44 (0) 20 7832 4343
david.abramson@globaldata.com
Bornadata (Bonnie) Bain, PhD
Global Head/EVP, Healthcare
Operations and Strategy
GlobalData+1 (617) 747 4136
bonnie.bain@globaldata.com
Revati Tatake, PhD
Global Director of Databases andAnalytics, Healthcare
GlobalData+1 (646) 395 5469
revati.tatake@globaldata.com

Deloitte project team
We would like to acknowledge the significant contribution of the following people tothe analysis, research and drafting of this report, without which this report would
not have been possible: Kevin Dondarski, Pavithra Rallapalli, Evelyn Chu, Hawo-Kiin
Ali, Nick Wright, Matthew Thaxter, Chloe Ambery, Youcef Mahfoufi, Andy Bolt, Dave
Nestor, Carmen Jack and Pratik Avhad.
GlobalData Contributors
We would like to acknowledge the contribution of Cynthia Brisac (GlobalData
Pharma Consulting) and GlobalData’s Therapy Analysis and Database teams
Contact informxation
To see more research and sign up for future publications visit:www.deloitte.co.uk/centreforhealthsolutions

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