2012 EY Biotech Report

Beyond borders
Global biotechnology report 2012

To our clients and friends:
Welcome to the 26th annual issue of Beyond borders, Ernst & Young’s annual report on the global
biotechnology industry.
Our analysis of trends across the leading centers of biotech activity reveals both signs of hope and causes for
concern. The financial performance of publicly traded companies is more robust than at any time since the
onset of the global financial crisis, with the industry returning to double-digit revenue growth. Companies
that had made drastic cuts in R&D spending in the aftermath of the crisis are now making substantial
increases in their pipeline development efforts.
But even as things are heading back to normal on the financial performance front, the financing situation
remains mired in the “new normal” we have been describing for the last few years. While the biotech
industry raised more capital in 2011 than at any time since the genomics bubble of 2000, this increase
was driven entirely by large debt financings by the industry’s commercial leaders. The money flowing to the
vast majority of smaller firms, including pre-commercial, R&D-phase companies — a measure we refer to as
“innovation capital” — has remained flat for the last several years.
As such, the question we have posed for the last two years is more relevant than ever: how can biotech
innovation be sustained during a time of serious resource constraints? In this year’s Point of view article,
we offer a perspective that addresses not just the challenges in the changing health care ecosystem but
also the latent opportunities. The paradigm we present — the holistic open learning network, or HOLNet —
takes advantage of health care’s move to an outcomes-focused, patient-centric, data-driven future.
HOLNets could fundamentally change how R&D is funded and conducted, by bringing together a diverse
range of participants, encouraging the pooling of precompetitive data and permitting researchers to learn
in real time from each others’ insights and missteps.
These are timely topics, and we look forward to exploring them with you — and helping each other learn in
real time — through our Global Life Sciences Blog and other social media venues. Please look for information
about the blog on ey.com/lifesciences in the months ahead and join the conversation. Ernst & Young’s global
organization stands ready to help you address your business challenges.
Gautam Jaggi
Managing Editor, Beyond borders
Glen T. Giovannetti
Global Biotechnology Leader

1 Perspectives
1 Point of view
HOLNets: learning from the whole network
2 Focusing on what you do best
• Bruce Booth, Atlas Venture
5 Collaborative innovation
• David Steinberg, PureTech Ventures
7 More pharma spinoffs?
• Ron Cohen, Acorda Therapeutics
8 Case study: Coalition Against Major Diseases (CAMD)
• Marc Cantillon, Coalition Against Major Diseases
9 Case study: One Mind for Research
• Magali Haas, One Mind for Research
10 Leveraging our strengths
• Samantha Du, Sequoia Capital China
12 Making it happen: building collective intent care networks to change health care delivery
• Sanjeev Wadhwa, Ernst & Young
15 Getting personal, getting networked
• Christian Itin, Micromet
17 Partnering for specialization
• John Maraganore, Alnylam Pharmaceuticals
20 Patient-centric innovation
N. Anthony Coles, Onyx Pharmaceuticals
21 Protecting the biotech ecosystem
Moncef Slaoui, GlaxoSmithKline
22 To boost R&D, stop flying blind and start observing
Joshua Boger, Vertex Pharmaceuticals
25 Financial performance
Recovery and stabilization
27 • United States
32 • Europe
36 • Canada
37 • Australia
39 Financing
Innovative capital
51 • Europe
55 • Canada
59 Deals
Pharma recalibrates
69 • Europe
71 • Canada
73 Products and pipeline
Promising signs
81 Acknowledgments
82 Data exhibit index
84 Global biotechnology contacts
Contents

1
Point of view
HOLNets: learning from the
whole network
The same old new normal
Over the last two years, we’ve written
extensively about the global financial crisis
and the “new normal.” This has mainly
been a new normal for capital markets
and financing, with implications for the
biotechnology industry because of the
capital-intensive nature of biotech R&D.
Investors and companies have responded
with creative approaches to make R&D
more efficient and sustainable. They have
tweaked the existing drug development
paradigm (e.g., fail fast approaches) and/
or made reductions in operating costs
and overhead (e.g., asset-centric models,
outsourcing, virtual business models).
These efforts continued over the last year.
We have even seen the emergence of some
new models (e.g., pharma/VC strategic
partnerships — more on these later).
However, these creative approaches are
making only marginal improvements to
a funding and innovation business model
that, while under unprecedented strain in
the current environment, has long grappled
with basic tensions. Gary Pisano of Harvard
University, for instance, has pointed out
that intellectual property (IP) is highly
fragmented in the biotech industry, in part
because the murky and complex nature of
IP and IP law makes companies unwilling
to share. This inevitably wastes resources
as companies duplicate efforts. In prior
issues of Beyond borders, we have similarly
pointed to the timing mismatch between
the investment horizons of venture funds
and drug development time frames. Such
tensions did not matter much as long as
investors were willing to put up the large
sums of capital required to fund drug
development, and as long as they could
earn returns high enough to keep them
coming back. In the new normal — as the era
of easy money and high leverage has ended
and as numerous pressures have squeezed
VC multiples — both of those preconditions
have come under increasing strain. In the
aftermath of the financial crisis, therefore,
the tensions that have always existed
beneath the surface have bubbled to
the top. The underlying inefficiency and
redundancy of drug development have
become particularly incongruous in the
current financing climate — an extravagance
we can no longer afford. The solutions
we’ve seen so far, while very creative and
innovative, are largely tinkering around the
edges — refinements and adjustments to
a long-standing drug development model.
They lead to incremental improvements in
efficiency but are unlikely to change the
numbers in a fundamental way.
What we need, more than ever, is a new
paradigm — something that radically
rethinks the ways in which scientific
insights are gained and translated into new
products, and which creates new ways of
assembling resources to fuel this important
endeavor. In this year’s Point of view article,
we present one such solution, something we
call holistic open learning networks. It’s an
idea that builds on trends already visible in
the market and, more importantly, involves
learning from beyond the life sciences
industry and leveraging the strengths of a
diverse range of entities — from providers
and patient groups to social media networks
and data analytics firms.
For much of the past, learning from
the outside has been a particularly
acute challenge for big pharma, as the
“not invented here” mentality led large
companies to dismiss innovative ideas
that did not originate from their own labs.
Pharma companies paid a steep price for
this closed mindset, as emerging biotech
companies stole the lead in developing
new generations of game-changing
platforms and efficacious new products.
Today, pharmaceutical companies have
a more outside-in approach. Not only
has big pharma come to rely on biotech
for a significant portion of its pipeline,
but pharma companies are also boldly
experimenting with new business models to
prepare for a future in which success will be
determined by not just drug sales, but also
the ability to demonstrably improve health
outcomes. To develop these models, pharma
companies are beginning to experiment
with partnerships with other life sciences
firms as well as a range of companies from
other industries: health care providers and
payers, information technology companies,
mobile telephony providers, retailers and
others. (For a deep discussion of pharma’s
“Pharma 3.0” business model innovation,
refer to the 2010–12 issues of our sister
publication, Progressions.)
In many ways, it is now time for the drug
development side of the industry (including
biotech) to do the same. Even as the
health care ecosystem around us is being
completely reinvented in response to
unsustainable increases in health costs, the
drug development paradigm has remained
essentially unchanged. To understand
where the opportunities lie for reinventing
drug development, let’s start by revisiting
how health care itself is changing.
Point of view HOLNets: learning from the whole network
Even as health care is being
completely reinvented in response
to unsustainable increases in
costs, the drug development
paradigm has remained
essentially unchanged.

2
Outcomes, technology and big data: the new ecosystem
Even as biotech adjusts to its new normal, we are in the midst of
other seismic shifts in the health care ecosystem, all of which have
implications — both challenges and opportunities — for companies in
the business of drug development.
The shift is being driven by two trends that are occurring
simultaneously. The first — the need to make health care costs
sustainable — is driving payers to change incentives. Through
a range of health care reforms across key markets, payers are
focusing increasingly on health outcomes. Systems are shifting
away from paying for products and procedures and toward paying
for performance. This is playing out in multiple ways: comparative
effectiveness research, prevention and disease management
programs, payment regimes that shift financial risk to providers and
in some cases drug companies, and more. The bottom line for life
sciences companies is that they will increasingly find themselves in
the business of changing patient behaviors and delivering health
outcomes rather than purely in the historic business of selling
products.
Accompanying the increasingly urgent need to bring health care
costs under control is the emergence of the second driver of
change — an explosion of new technologies that have the potential
to make health care delivery radically more efficient. Electronic
health records, which have existed in concept for decades but never
really gained much traction, are being used in larger numbers than
Beyond borders Global biotechnology report 2012
Focusing on what you do best
Bruce Booth, PhD
Atlas Venture, Partner
The simple answer to sustaining innovation is that each player in the ecosystem should focus on what it does best.
Academics should focus on basic research and early biologic target validation. Start-ups, with experienced talent, unleashed from
the constraints of big-company behavior, should experiment with lots of approaches for high-value, emerging targets: biological
variables (e.g., different drug modalities such as NCEs, mAbs, peptides, antisense and RNAi), clinical variables (e.g., patient subtypes,
new translational designs, repurposing and indications discovery), organizational variables (e.g., virtual CRO-enabled models vs.
fully integrated teams) and business model variables (e.g., drug platforms vs. single-asset companies). We should let a lot of start-up
flowers bloom. Some will grow, some won’t.
Venture capitalists — traditional and corporate venture funds along with alternative capital providers such as angel investors and
philanthropic foundations — should strive to allocate resources to winning experimental start-up models. Importantly, these capital
providers need to be disciplined about reducing the costly false positives in drug research and reallocate to new opportunities more
efficiently.
Big pharma companies should participate in this diverse research ecosystem through “open innovation” strategies that pair up their
deep capabilities (e.g., chemical libraries, biologics technologies) and creative partnering power with the agility, specific expertise,
and passion of the start-up culture. As valuable, high-impact medicines emerge from this research ecosystem into the later stages of
clinical development, big pharma should bring its balance sheet and unparalleled global development and marketing capabilities to
successfully drive new drug approvals and commercial launches.
Sharing value across these various elements from target validation through product sales would help foster a vibrant, healthy
ecosystem. Of course, this is all great in theory. Unfortunately, things such as legacy infrastructure, cultural differences, decision-
making inertia, frictional costs, resource misallocation, misperception of risk, and winner-take-all mindsets conspire to make this
efficient ecosystem a challenge. But that doesn’t mean we shouldn’t keep trying.

3
ever before, as policy makers increase incentives for adoption.
Mobile health technologies have taken off in a big way, as an
incredible variety of smartphone apps are empowering patients with
more transparent information and a greater ability to monitor and
manage their own health. Health-specific social media platforms
have emerged, allowing patients and physicians to interact with
their peers and with each other to discuss their progress and side
effects — and learn from each other in real time. A sea of sensors —
embedded not just in new generations of medical technology
products but even in everyday objects such as mobile phones,
weighing scales, running shoes, sportswear and wristwatches —
are providing real-time feedback to patients and their caregivers,
allowing for better management of health and a greater focus on
prevention.
Since all of these technologies are generating massive amounts of
data, a significant corollary of the changing ecosystem is health
care’s move to the era of big data. We are already seeing dramatic
increases in the amount of data being generated from numerous
sources — genomic research, clinical trials, electronic medical
records, wireless devices, smartphone apps and social media
platforms, to name a few — with the volume expected to grow
exponentially. The 1000 Genomes Project — an initiative to analyze
large amounts of genomic data to find genetic variants that affect
at least 1% of the population — has already built a data set that
is 200 terabytes in size, the equivalent of 16 million file cabinets
worth of text. Across the US health care system, it is estimated
that the amount of data crossed the 150 exabyte threshold
(150 billion gigabytes) last year. But big data is not just about
more information. It’s also about more types of information (e.g.,
health records, medical claims data, social media threads, imaging
data, video feeds, data from sensors) from more diverse sources.
While drug development companies have always been steeped in
a culture of data (indeed, their very success has depended on the
quality of the clinical trial data they generate), in the era of big
data, most of this information will be generated in real time, will be
controlled by others and will cut across the value chain, from R&D
to health care delivery.
In the 2010 issue of Progressions, we discussed how big data
is driving a new trend with tremendous implications for drug
companies. Payers and others are mining electronic health records
and other data to identify correlations and make assessments about
interventions and standards of care. This development — something
we termed “value mining,” or the use of data mining to make
decisions about the relative value of products and interventions —
means that other entities are making decisions about drug
companies’ products using data that is outside the control of these
firms. Even more compelling, value mining is much quicker and
cheaper than the way drug companies have traditionally gained
insights about the value of medical products — the extended process
of hypothesis testing through clinical trials.
To date, value mining has only happened at the commercial end of
the value chain, to assess the value of marketed products. But what
if the power of big data could be harnessed to similarly develop
quicker, real-time insights about candidates in the pipeline? How
much power could we unleash by connecting the dots between the
huge volumes of data scattered across the ecosystem? How do we
achieve this potential and who could take the lead? We think these
are compelling questions, and we turn to them next.
Point of view HOLNets: learning from the whole network
What if the power of big data could be harnessed to
similarly develop quicker, real-time insights about
candidates in the pipeline?

4 Beyond borders Global biotechnology report 2012
Reinventing R&D: learning from the ecosystem
Over the last few decades, as the emergence of the modern
biotechnology industry introduced new technologies to the drug
development process, there has been considerable innovation in
the capabilities used to conduct R&D. Combinatorial chemistry
and high-throughput screening have brought industrial-scale
processes to drug development by allowing for the automated
generation and testing of enormous numbers of potential drug
candidates. Pharmacogenetics and other personalized medicine
approaches have created the potential for developing therapeutics
that are vastly more targeted and efficacious on individual patients.
Meanwhile, the emergence of bioinformatics brought with it the
promise of bringing drug development into the information era,
by enabling the use of computers for understanding disease
mechanisms, predictive modeling, drug synthesis, testing and more.
Yet, despite these innovations, R&D productivity has not improved —
drug approvals have not increased to any appreciable degree,
while development costs have escalated. Indeed, the process of
developing drugs has remained unchanged in several key respects.
Despite the new technologies that have been introduced, drug
development is still linear, slow, inflexible, expensive and siloed:
• Linear. Drug R&D is conducted in a stepwise manner. Through
a series of preclinical studies and clinical trials in sequentially
larger populations, researchers seek to answer questions related
to safety, efficacy and dosages.
• Slow. The process of taking a compound or molecule from
early research to approved product takes well over a decade. In
essence, researchers come up with an idea and then wait years
to find out whether it works.
• Inflexible. The drug development process is also very rigid.
This is particularly tragic given the length of the process.
Over months and years of trials, valuable information is being
gathered. Yet, the double blinding of trials effectively means
that researchers can only learn at a few points along the process
— when the current phase of clinical trials is completed and the
data analyzed. There is little ability to learn continuously and
adjust one’s approach based on real-time information.
• Expensive. An inevitable consequence of this slow and inflexible
process is that drug development has become increasingly
expensive. On average, companies spend well over US$1 billion
to bring an approved drug to market (a number that includes the
cost of products that fail along the way).
• Siloed. Lastly, the R&D process is highly fragmented. Driven by
the need to protect their intellectual property, companies fail to
learn from experiences and the mistakes of others.
Of course, there is good reason for much of this, including
regulatory requirements that define the approval process and the
fact that firms have always succeeded or failed on the strength
of their intellectual property. But we can no longer afford to keep
doing things this way, particularly in today’s resource-constrained,
escalating-cost environment. Instead of a drug development
paradigm that is linear, slow, inflexible, expensive and siloed,
we desperately need one that is iterative, fast, adaptive, cost-
efficient and open/networked.
We are already seeing examples that are taking us in this direction.
As discussed extensively in the last two issues of Beyond borders, a
host of new approaches are attempting to make drug development
faster and more cost-efficient, from fail-fast R&D paradigms to
asset-centric funding models that attempt to get to a value-creating
proof-of-concept milestone with minimal overhead. To make R&D
more fast, iterative and adaptive, there has been a growing focus
on adaptive clinical trials. In one particularly noteworthy example,
the I-SPY2 trial, three drug companies are collaborating to screen
multiple breast cancer drugs, each targeting a different pathway.
The trial has an adaptive design under which patient outcomes are
immediately used to inform treatment assignments for subsequent
trial participants. The trial designers claim that I-SPY 2 can test
new treatments in half the time of standard trials, at a fraction of
the cost and with significantly fewer participants. Meanwhile, we’ve
seen several examples of more open approaches, from an uptick in
precompetitive collaboration to GlaxoSmithKline’s contribution of
intellectual property for neglected tropical diseases.
To take such efforts to the next level, we now need mechanisms for
breaking down silos more broadly. We need processes for sharing
information and learning from the ecosystem in real time. We
need to move to a world in which the division between the R&D
and commercial ends of the value chain becomes increasingly
meaningless because scientists and practitioners are continuously
gaining insights from data being generated across the value chain
and throughout the cycle of care. To achieve all of this, we propose
the widespread use of holistic open learning networks (HOLNets).

The four characteristics embedded in this moniker don’t just define
HOLNets — each one of them is also a critical requirement for the
success of this approach:
• Holistic. The HOLNet approach represents a vastly different
and inclusive approach to R&D. The boundaries between drug
development, product commercialization and health care
delivery are blurred. Rather than being confined to the traditional
siloed and sequential approach to drug development, HOLNets
would share data and connect dots across the entire value chain
of companies (from early research to marketing) and cycle of
care of patients (from prevention to cure).
• Open. One of the biggest changes in the HOLNet approach is
openness. While the specific rules of each HOLNet will depend on
the needs and preferences of its members, these networks will
typically require that members pool their strengths and assets
(e.g., talent and precompetitive data). They will also involve
sharing any resulting output (e.g., creating open standards,
making insights available to all members and often to non-
members as well). This is one of the most powerful aspects of a
HOLNet, since it has the potential to make R&D radically more
efficient and productive, by reducing redundant expenditures
and allowing researchers to learn from each others’ insights and
mistakes. But in an industry where companies have historically
operated under shrouds of secrecy, this is also one of the
biggest obstacles to the adoption of HOLNets by life sciences
firms. Consequently, HOLNets will need to address intellectual
property concerns by clearly defining precompetitive information
that is available for sharing as opposed to information that is
proprietary. It is encouraging that the term “precompetitive”
is being used more broadly in recent years, as companies
grow willing to collaborate in areas once considered sources of
competitive advantage. But we believe that the notion of the
precompetitive space will have to expand even further, changing
to some extent the very basis of competition. For example, while
it is entirely appropriate for companies to compete based on
the effectiveness of molecules they discover, is it essential that
they compete on all underlying technologies (e.g., biomarkers)
and even on processes such as clinical trial enrollment?
Companies are growing increasingly comfortable with the
notion of collaborating with competitors, and as we’ll see later,
early examples of networks that are taking open approaches to
intellectual property have had no problem attracting large and
small life sciences members.
• Learning. Above all, HOLNets are about learning — their
raison d’être. But while learning in the drug development process
has historically been slow, sequential and siloed, HOLNets are
about learning rapidly, in real time, by connecting data from
across the ecosystem. Real-time learning allows constituents to
quickly adjust their approaches — from clinical trials to standards
of care — saving time and money and increasing success rates.
But to learn from big data, we need standards that allow data to
be combined as well as sophisticated analytics to mine insights —
capabilities that HOLNets will need to enable and foster.
• Network. Last, but not least, a HOLNet has to be a network.
Radically reinventing R&D and unleashing the transformative
potential of big data requires the participation of diverse players
from across the ecosystem. The network needs a common goal —
a collective intent — around which all its members are aligned.
Different entities don’t all need to do the same thing — indeed,
given their diverse backgrounds and strengths, it would be
better if they didn’t — but they do need to be pulling in the same
direction. The optimal network size and participants will depend
on the challenge being addressed.
5Point of view HOLNets: learning from the whole network
Collaborative innovation
David Steinberg
PureTech Ventures, Partner
To sustain innovation, we could do two things. First, let’s reduce
redundant research. There’s no reason every pharma has to
study fundamental biology in every research area, wasting
hundreds of millions of dollars in the process. Academics, NIH,
pharma and entrepreneurs should work together to explore
new biology areas and get them “drug development ready.”
Pharma and biotech can access the assays, probes, etc., and do
their own drug development. Second, let’s parallelize biotech
entrepreneurship. Typically, solo entrepreneurs work for two
years and then pitch to VCs, who then pivot, work for two to
three more years, and pitch to pharma, who then discard all but
the one program of interest. Let’s get entrepreneurs, VCs and
pharmas working together, at the same time, to start and fund
biotech start-ups around a common vision.

HOLNets in action
When all of this comes together — when an initiative is a truly
holistic, open, learning network with a diverse set of stakeholders —
it has the potential to tangibly transform the ways in which insights
are gathered and new drugs developed. In practice, a HOLNet could
play a critical role in:
• Pooling data. Because of their charter to be open and learn by
connecting diverse data sets, HOLNets can enable the pooling
of data in precompetitive spaces. This becomes particularly
compelling given their diverse membership, since these networks
could bring together genetic data from patients, claims data
from payers, outcomes data from providers’ EHR systems, data
on failed clinical trials from life sciences companies, insights from
disease foundations and more. They can create common pools
for their members (and perhaps non-members) to draw from,
including shared libraries and tissue banks.
• Creating standards. The pooling of data raises a corollary
question: standards. This is another area where HOLNets can
play a much-needed role. After all, data sharing means nothing
unless data can also be combined and studied holistically.
Without uniform standards and the ability to collectively analyze
this information, pooled data is not big data — it’s just a collection
of smaller data sets. Developing standards will also play a big
role in accelerating the creation of promising R&D tools, such
as biomarkers and disease models. And once again, by putting
these standards, assets and insights into the public domain, a
HOLNet can help make drug R&D vastly more productive and
efficient across the breadth of the ecosystem.
• Engaging regulators. As articulated earlier, much of what life
sciences companies and other health care entities do is defined
by regulatory regimes. To truly unleash the potential of the
HOLNet approach, it will be essential that regulators adapt to
a world in which insights can be gathered in real time through
more flexible approaches. As entities that represent a broad
coalition of partners — often focusing on diseases that are
becoming high priorities for policy makers — HOLNets will have
the credibility to engage with regulators and/or to encourage
new approaches to R&D and clinical trial design. The good news
is that regulators recognize the need to move in this direction.
Senior leaders from the FDA, for instance, have gone on
record encouraging experimentation, and there is at least one
compelling example of a flexible, learning clinical trial paradigm —
the I-SPY2 trial discussed earlier.
• Engaging patients. But regulators are not the only entities
with which HOLNets will engage. The new ecosystem is, in
essence, a patient-centric world, and HOLNets will need to
engage with patients by identifying relevant populations
(perhaps with the assistance of disease foundations), developing
ongoing relationships with them and collecting their data
with their informed consent. This has the potential to enable
better outcomes through an increased focus on prevention
and health management, but it also has tremendous benefits
for drug R&D. Based on these ongoing relationships and a
deeper understanding of patients’ real-world experiences with
their conditions and medications, HOLNets can provide new
insights for the drug development process. In addition, it may
be possible to substantially speed up clinical trial enrollment.
The existing paradigm of clinical trials — in which a hypothesis is
articulated first, a trial protocol is developed next and only then
do researchers start looking for appropriate patients to enroll
— can be turned on its head. A world in which HOLNets have
existing patient relationships and databases with comprehensive
information — including contact information, genetic profiles,
conditions, disease states and prior treatments — is one in which
patients have in essence been pre-screened. With patients
already identified (and perhaps consent to participate in trials
obtained in advance) appropriate individuals can quickly and
easily be enrolled once a suitable trial comes along.

To achieve their full potential, HOLNets will need an organization
at the center to orchestrate all these activities and to balance the
needs and priorities of members. The organization needs dedicated
human resources, which could be a combination of full-time
employees and talented individuals on secondment from member
organizations.
We are seeing examples that do much, but not all, of this. At the
R&D end of the value chain, the charge is being led by disease
foundations and other nonprofits, often focused on brain diseases.
For instance, the Alzheimer’s Disease Neuroimaging Initiative
(ADNI), an early example of this sort of collaboration, was set up in
the early 2000s to identify biomarkers that show the progression
of Alzheimer’s. The initiative — a public-private-partnership with
funding from the National Institutes of Health as well as private
partner support from various companies and associations —
deliberately took an open approach to information. All data coming
out of its studies are immediately released to the public over the
internet. NewDrugs4BadBugs, a new European initiative to combat
antibiotic-resistant bacteria, is bringing together several entities,
including the Innovative Medicines Initiative, GlaxoSmithKline,
Sanofi, AstraZeneca, Janssen and Basilea. The initiative plans
to share data openly, develop better networks of researchers
and create more fluid trial designs. Two other initiatives in the
neuroscience space — One Mind for Research and the Coalition
Against Major Diseases — are profiled in greater depth in the
accompanying articles by Marc Cantillon and Magali Haas on
pages 8 and 9. These initiatives and numerous other examples —
the CommonMind Consortium, the Biomarkers Consortium, the
Structural Genomics Consortium, the Multiple Myeloma Research
Consortium and others — are seeking to assemble a diverse network
of actors to pool data, develop common standards and accelerate
research, often with the requirement that data and insights be
shared openly.
More pharma spinoffs?
Ron Cohen, MD
Acorda Therapeutics, President and CEO
We’re already seeing the ecosystem trying to adapt to the
challenge of sustaining innovation. For example, some VCs are
now emphasizing single-product plays, designed to be advanced
to a value-creating clinical endpoint with minimal infrastructure
and cost, relying heavily on outsourcing to CROs and with
the aim of selling to a pharma company once milestones are
attained. Some are entering into funds together with pharma
companies to leverage both capital and expertise — though this
may not be truly novel, since many pharma companies have had
their own venture arms for years. In the recently announced
partnership between Johnson & Johnson, GlaxoSmithKline and
Index Ventures, the pharma companies will get a first look at
early-stage projects funded by the Index fund. Sanofi and Third
Rock Ventures have an arrangement that is focusing on single
product plays. Several pharma companies are trying to inject a
biotech-like risk-taking ethos into their discovery/development
programs, by creating smaller, more focused units, often with a
mandate to partner with outside academic and biotech
groups as needed (e.g., GlaxoSmithKline’s Discovery
Performance Units).
One could imagine more variations. Pharma might actually
create mechanisms for some of its talent to propose ideas for
spinoff companies based on ideas or products in development,
giving the pharma right of first offer after certain milestones
are attained. The companies could be funded by the pharma
company or in partnership with a VC fund.
continued on page 10
When all of this comes together — when an initiative is
a truly holistic, open, learning network with a diverse
set of stakeholders — it has the potential to tangibly
transform the ways in which insights are gathered and
new drugs developed.

Marc Cantillon, MD
Former Executive Director and R&D Consultant
Case study
Coalition Against Major Diseases (CAMD)
Behavior originates in needs and signals from the environment.
So my decision to be a founding member of the Coalition Against
Major Diseases (CAMD) in 2008 was guided by my needs. At
the time, I was head of neuroscience clinical development
at Schering-Plough and was seeking a neutral venue for the
precompetitive sharing of development tools. As a collaborative
effort to accelerate the translation of scientific discoveries
into treatments, CAMD — a part of the Critical Path Institute —
provided such a venue. The coalition, which focuses on diseases
in which the efforts of individual pharma actors have made little
headway, took on Alzheimer’s disease (AD) and Parkinson’s
disease (PD) as its first challenges.
CAMD members are scientists from pharma, biotech, universities,
government organizations (e.g., FDA and NIH) and patient/
voluntary health organizations. All members must abide by
a uniform charter, which requires them to share data and
contribute time and energy. To avoid even a perceived conflict of
interest, CAMD doesn’t accept money from the pharma industry,
relying instead on competitive grants. A uniform charter requires
that any IP developed by the consortium is shared — creating a
pool of knowledge and assets that could truly accelerate R&D —
while allowing members to protect material that they developed
independently of their collaborative efforts. Yet, IP concerns have
in no way hampered the ability to attract life sciences members.
To the contrary, these companies see a net benefit from joining.
The advantages of being a member of CAMD span the three areas
in which the coalition is focused: data sharing, disease modeling
and biomarkers.
• Data sharing. CAMD, working with the Clinical Data
Interchange Standards Consortium, has done groundbreaking
work in creating common AD data standards — something no
company could achieve alone. Indeed, different standards
even within companies prevented ﬁrms from combining their
own clinical trials, much less those of multiple entities, in
a common database. For instance, there was no consistent
way of recording answers to questions on memory, recall,
etc., that are part of the Alzheimer’s Disease Assessment
Scale-Cognitive subscale (ADAS-cog, the main benchmark
for measuring outcomes). In June 2010, CAMD solved this
problem when it created a standard database combining the
placebo arms of members’ Alzheimer’s trials. Data sharing
and common standards could drive faster drug development
and faster review by the FDA. Today, we are in the early stages
of an explosive increase in the amount of health data, and
it’s happening faster than most people probably would have
anticipated. But again, unless these data are collected in a
standardized way, their true potential will not be realized.
• Disease modeling. Standardizing and sharing data will
allow for the development of disease models to understand
differences in progression in different categories of patients
(e.g., by age, ethnicity and medication). This does not need to
be limited to traditional research settings. With large volumes
of secure anonymized data, there’s no reason we couldn’t use
these assets to better understand when memory problems
begin, identify early warning signs and track the course of the
disease — which could help develop new treatments and also
boost prevention. To be used in the regulatory context, these
disease models need to be widely accepted. It can be a tough
sell for a drug company to get the FDA to accept proprietary
disease models when the agency hasn’t had a say in vetting
these models. But all of that changes in a coalition like CAMD,
where the FDA is a member and actively involved in developing
disease models.
• Biomarkers. Biomarkers have tremendous potential for helping
us understand disease mechanisms and subtypes and selecting
patients in clinical trials. Once again, we need standards —
biomarkers that are validated, standardized and qualiﬁed
for use — and a single company will have less credibility and
resources to establish standards than a collaborative effort.
Approaches such as CAMD are not just for large pharma; they are
equally relevant and useful for R&D-stage biotech firms. While
small companies may sometimes see biomarker identification
as a source of competitive advantage, the truth is that they
stand to benefit from establishing standards in such areas. In
addition, members can gain early access to the FDA — potentially
working to create review standards and gaining early visibility
into standards as they are being created. For too long, our
industry has resisted sharing. Today, we are seeing a rethinking
of where companies should compete and where they might join
forces. Collaborations such as CAMD can be critical in sharing
data, establishing standards and accelerating the development of
much-needed cures.

Magali Haas, MD, PhD
Incoming Chief Science & Technology Officer
Case study
One Mind for Research
One Mind for Research was founded by Patrick Kennedy (son of
the late Senator Ted Kennedy) and Garen Staglin (co-founder of
the International Mental Health Research Organization). They
launched the One Mind Campaign on 25 May 2011, the 50th
anniversary of President John F. Kennedy’s famous “moon-shot”
speech.
One Mind’s mission is to accelerate neuroscience research so that,
within a decade, all humanity can experience a lifetime free of
brain disease. Like President Kennedy’s moon shot, this is a bold,
audacious goal, but we feel that without an ambitious target, we
will not get the urgency, resources and alignment that’s needed.
A collaborative approach is critical for this challenge. The brain
is the most complex organ in the human body and also one
of the most inaccessible. To date, neither academia nor the
pharmaceutical industry has fully understood the mechanisms of
the brain. We will need the holistic involvement of all stakeholders
— industry, governments, patients, academic organizations,
advocacy groups — each of which holds a piece of the puzzle.
Historically, the level of investment in brain research has not
been proportionate to the burden this disease imposes on society
— something we are only starting to appreciate. Last year, for
the first time, the European Brain Commission estimated the
aggregate burden imposed by 19 brain disorders on that society.
The total they came up with was a staggering US$1 trillion. For
the first time, Europe recognizes that this is the number one
priority for their health care agenda.
We have yet to do such a comprehensive assessment in the US,
where we still look at brain disorders disease by disease instead of
thinking of the brain as one organ system. However, a preliminary
independent study conducted for the US came up with a similar
estimate for the economic burden in this country.
When you add to that the social stigma still associated with these
conditions, one can appreciate why we haven’t focused on how
burdensome brain disorders are and how much the loss of mental
capital constrains our society.
Compounding the challenge, we are now seeing a reduction in
investment in brain research. Investors have become frustrated
by relatively low returns on investment, driven by the poor
understanding of brain disease mechanisms and the inability to
translate basic science to advance the drug pipeline.
To really change things, we have to change the way we work. That
is what One Mind is attempting to do, by gathering resources,
aligning stakeholders, prioritizing an agenda, promoting a culture
of sharing, transforming public policy and eliminating stigma. We
see ourselves as a central trusted third-party organization whose
single mission is to accelerate the development of preventions and
cures and eliminate the silos that have slowed our progress.
We are going about this in three ways. First, we are raising
awareness about the impact and burden of these disorders. This
is critical for building public support among policy makers and the
public and making brain disorders a top priority on the health care
agenda.
Second, we are seeking to de-risk the model to stimulate
investment. To do this, we will need to accelerate research by
generating a knowledge base across disorders, understanding
mechanisms of action, conducting large-scale trials, identifying
biomarkers and developing disease models. All of this requires
combining data from various stakeholders — not just clinical trial
data, but also real-time information from patients about their
conditions.
Third, we are trying to alter the policy landscape. This
encompasses everything from incentive models to motivate
cooperation and sharing, to regulatory constructs, IP patent
constructs and more. These are all issues that need to be
systematically addressed.
Our corporate partners are essential to this effort. It will take the
combined capabilities of companies from numerous industries —
pharma, biotech, information technology and others — to advance
this field. Depending on their strengths, companies can contribute
different assets — data, platforms, imaging capabilities and, of
course, financial investments. We hope that many companies will
allow representatives from their organizations to work with us in
workshops or even do rotational fellowships with us.
The One Mind model is every bit as relevant for early-stage R&D
companies, and we have a number of small biotech companies as
members. At a time when the FDA is revising its guidelines for
medical device companies and biomarker platforms, companies
in these areas need evidence that their platforms provide valid
clinical insights. This typically requires larger studies than small
companies can afford. But in this cooperative model, small
companies can test their platforms against the large datasets,
which is far quicker and more cost-effective than if they tried to
generate their own large datasets. Similarly, a small therapeutic
company will benefit from the disease models One Mind is
building, which will allow them to pursue personalized medicine
approaches more efficiently.
One Mind is already reshaping the boundaries between
competitive research and precompetitive collaboration. For
example, we are developing disease models that we plan to put
into the public domain. Not too long ago, this was an area where
companies would have wanted their own unique IP protected
models. To accelerate the development of new cures, such shifts
are long overdue.

One organization that is playing a central role in driving for more
open drug development is Sage Bionetworks, a Seattle-based
nonprofit organization that was founded in 2009. One of the
organization’s first initiatives, the Sage Commons, has created
an “open source community where computational biologists can
develop and test competing models built from common resources.”
The Sage Commons platform allows for integrating large data sets
from various health ecosystem constituents and making them freely
available for genomics analysis and predictive disease modeling.
Earlier this year, Sage announced the creation of Portable Legal
Consent (PLC), a potentially game-changing standard that reverses
the way in which consent is typically obtained from patients. Anyone
participating in a clinical trial or having their genome sequenced
would now have the option of making their data available to any
researcher who accepts the terms of the PLC approach (including
the requirement that any discoveries from this data must also be
put in the public domain). The data is anonymized and Sage has
gone to considerable lengths to make sure that consent is truly
informed (e.g., through online tutorials that cannot be bypassed).
With PLC, researchers would save time, because they do not have to
obtain consent from subjects every time they initiate a new study,
while patients could have greater confidence that any use of their
data will comply with a standard set of rules. Perhaps the most
promising implication of the PLC approach, though, is that having
a widely adopted standard for consent could allow for data sets to
be combined and analyzed in aggregate — unleashing the power of
big data.
At the same time, we are also seeing examples at the other end
of the value chain — health care delivery. For instance, Sanofi has
recently partnered with the Baltimore County Department of Aging,
the John A. Hartford Foundation and the National Coalition on
Aging on a pilot program to help doctors connect older diabetics
with evidence-based education and wellness support. Merck & Co.
has partnered with the Camden Coalition of Healthcare Providers
to create the Camden Citywide Diabetes Collaborative to implement
comprehensive diabetes prevention and management programs
in the city of Camden, New Jersey. Similarly, Eli Lilly and Company
is partnering with Anthem Blue Cross Blue Shield and five Indiana-
based health care providers to achieve better health outcomes
for diabetes patients. (For more on how collaborative network
approaches are transforming health care delivery, refer to the
article by Sanjeev Wadhwa on page 12.)
Many of these initiatives — at both ends of the value chain — have
key aspects of HOLNets. They are networks that bring together a
diverse set of actors. They are often open, insisting that information
be shared openly to facilitate greater learning. For the most part,
though, they are not holistic, in that they are still confined to
traditional definitions of R&D and commercial delivery.
Over time, we believe there is a case for more of these initiatives
to expand across the value chain, to truly unleash the power of
data being generated throughout the ecosystem. In particular, as
discussed below, we think that pharma companies could play a big
role in driving the widespread adoption of these networks.
Leveraging our strengths
Samantha Du
Sequoia Capital China, Managing Director
By leveraging our strengths, biotech firms, investors and
pharma companies can effectively increase R&D productivity
and efficiency. Biotech’s strengths are its entrepreneurship,
operational efficiency (much less bureaucracy) and focus,
while pharma can contribute high-quality late development and
commercialization excellence. Biotech start-ups need to think
very early about partnering with pharma companies to access
their domain expertise. Investors will continue to be critical in
today’s challenging business climate. For a resource-constrained
biotech start-up, it is crucial to work with investors that can
provide not just capital but also appropriate knowledge and
networks. Lastly, non-dilutive capital from governments and
foundations can be very helpful in today’s resource-constrained
environment.
A key part of the solution will be robust and relevant regulatory
regimes. In a highly regulated industry such as ours, regulators
(e.g., the FDA, EMEA and SFDA) are key ecosystem stakeholders.
Without efficient and progressive regulators, no amount of
effort from biotech and pharma will change the productivity and
capital efficiency of the drug development model.

Getting there
While the HOLNet is a compelling vision of a future state that could
make drug development vastly more efficient and productive, it
has always been easy to imagine utopian health care systems. The
challenge in this business is inevitably in how we get there. The
health care ecosystem is so complex and intertwined — with so many
competing constituencies and interests — that aligning incentives
and structures is no mean task.
The good news is that health care has never been more primed for
this sort of collaborative approach. The unprecedented pressures
that many of its denizens now face — from payers wrestling with
runaway costs and rapidly aging populations, to big pharma’s
pipeline challenges, to emerging biotech startups and investors
grappling with a strained innovation model — are starting to change
mindsets and dismantle long-standing barriers. This is being further
catalyzed by changing incentives, new technologies and new
sources of data — all of which play a key role in driving the shift to
HOLNets.
Now, more than ever, the approach we describe above is feasible
because it is in the self-interest of the entities that would need to be
part of it:
Big pharma
We think that the pharmaceutical industry is well positioned at this
point in time to play a major role in making this approach more
mainstream and widespread, for several reasons. For pharma
companies, the biggest challenge, of course, is the patent cliff over
which they are now plunging and the fact that their pipelines are not
robust enough to fill the significant revenue gaps that will inevitably
follow.
Pharma companies have been reacting to these challenges
by restructuring and sharpening their strategic focus. As it
becomes increasingly clear that companies cannot do everything,
everywhere that they have in the past, pharma firms are evaluating
which diseases, product segments and geographic markets are
most strategic for them — leading companies to sell or spin off entire
divisions while moving more aggressively into other segments.
As their strategies move in different directions — against a backdrop
of an ecosystem where innovation is under pressure — pharma
companies recognize that it is in their strategic interest to sustain a
robust ecosystem of innovative biotech companies and investors. It
is not surprising, therefore, that we have seen a dramatic uptick in
transactions in which pharma companies are partnering with VCs to
send more capital in directions that are strategic to them. In the last
few months alone, we have seen such partnerships between Shire
and Atlas Venture (to invest in rare diseases), GlaxoSmithKline,
Johnson & Johnson and Index Ventures (targeting early-stage
investments), Merck and Flagship Ventures and others.
But the ripple effects of pharma companies’ patent expirations
extend beyond their walls. As more and more products become
subject to generic competition, pharma will have less aggregate
capacity to engage in activities such as corporate venture capital,
strategic alliances and M&A transactions — all of which have
provided a continuing source of funding for biotech companies
even as financial investors (VCs and public markets) have become
more stringent. For this year’s Beyond borders, Ernst & Young’s
Transaction Advisory Services professionals have built a model
to estimate the reduction in big pharma’s “firepower” to support
the innovation ecosystem. By our calculations, the capacity of
the top 28 biopharmaceutical companies has already declined by
about 30% between 2006 and 2011. Much of pharma’s remaining
capacity will also be targeted for building their presence in higher-
growth emerging markets, rather than supporting innovation in
mature ones. With more patent expirations ahead, and continuing
pressures from investors (who expect continued high dividends
and stock repurchases), we don’t anticipate that this situation will
appreciably improve in the foreseeable future.
continued on page 14

Making it happen: building collective intent care networks
to change health care delivery
Sanjeev Wadhwa
Ernst & Young
While holistic open learning networks (HOLNets) have the
potential to reinvent drug R&D for biotech and pharma
companies — making drug development more efficient and
productive and enabling real-time learning — these networks
also have tremendous potential to reinvent both the delivery of
health care and the ways in which drug companies go to market
(their commercial models). After all, HOLNets are holistic by
definition, and as already articulated, they are expected to make
old demarcations, such as the distinction between the R&D
and commercial phases of product development, increasingly
irrelevant. And, in a construct that is open by design and built
for real-time learning, it stands to reason that there would be
opportunities for health care delivery to benefit from these
networks as well.
In other spaces, we have discussed the need for “collective intent
care networks” (CICNs) that would bring together providers,
payers, pharmacies, academic medical centers, pharmaceutical
industry researchers and non-traditional partners to deliver
health care in more patient-centric and outcomes-driven
ways. CICNs will be jointly accountable for delivering improved
health outcomes and will align the behaviors of all participants
around outcomes through financial and other incentives. These
networks will transform health care delivery by increasing patient
engagement, enabling remote health monitoring, expanding
access and building prevention into care.
CICNs are similar to HOLNets but with a focus on care delivery.
To realize their full potential, CICNs need to follow the four basic
principles of HOLNets, by being holistic in scope, being open by
design, encouraging real-time learning and building a network of
diverse participants. In fact, we expect that, even though such a
network starts with participants from the health care delivery end
of the value chain, over time it would find benefits in expanding
to include a more holistic set of participants, and would in turn
deliver benefits across the ecosystem.
The move to such networks is being driven, of course, by the
increasingly urgent need to make health care costs sustainable —
manifested in developments such as the passage of the Patient
Protection and Affordable Care Act in the US and the move
toward comparative effectiveness research in several major
markets. These trends are fundamentally changing health care
delivery. Payer incentives (and hence provider behaviors) are
driving the move to patient-centric health care organizations that
are fully aligned around patient outcomes and value.
Achieving better patient outcomes will, in turn, require that
providers get closer to patients and build long-term relationships
with them. Over time, we will move to a paradigm in which
patients enroll in lifelong protocols of care with specific payers
and/or providers. Having such enduring relationships will be
critical for improving health outcomes, since they will enable an
increased focus on preventive care and allow all stakeholders to
take a more holistic view of patients’ health and diseases.
In the next decade, patient-doctor relationships and health care
delivery will be radically different from how they are today. We
will move from a world in which care is delivered in just two
types of locations (hospitals and doctors’ offices) to a paradigm
in which care is delivered in the communities where patients
live. The emphasis will move toward virtual care and remote
health delivery with the majority of patients using integrated
CICNs staffed by collaborative teams of drug researchers, clinical
development scientists and health care providers. Seeking to
provide better care at lower cost, primary care teams will join with
community partners to address factors that affect a community’s
health. To achieve the triple aim of health care initiatives (i.e.,
enhancing patients’ experience of care, reducing per-capita
health care costs and improving population health) patient-
centricity will inevitably need to be transformed into community-
centricity. Advanced knowledge technologies, along with multi-
comorbidity epidemiology, behavioral interactions, ethnographic
commercial interventions, predictive patient profiles (“health
avatars”) and disease opportunity maps identifying undiagnosed
patients will allow people to take over many functions of primary
care for themselves.
As already articulated in this year’s Point of view article, HOLNets
promise to make drug development vastly more efficient and
productive, by allowing for R&D paradigms that are adaptive
and have the ability to learn from real-time data and the
insights and missteps of others. But such networks also provide
opportunities for payers and providers to learn from real-time
data. By connecting the dots between datasets that are currently
owned by individual entities, these networks will provide better
information on benefits, risks and relative effectiveness of
new therapies. They will enable greater access to affordable
treatments and more effective ways to measure unmet needs.

Drug companies can play a relevant role in CICNs by adopting
communities with the goal of improving health outcomes,
often within a specific disease. BMS, for instance, launched a
program in South Africa called Secure the Future to support
the development and evaluation of cost-effective, sustainable
and replicable models for providing care and support to
people living with HIV/AIDS in Africa. The program sought to
supplement the half hour of care that patients received at the
clinic with “23 ½ hours” of disease management, and ongoing
support was provided in patients’ homes and communities.
Similarly, the Merck Foundation has committed US$15 million
to fund the Alliance to Reduce Disparities in Diabetes, a public/
private partnership encouraging evidence-based collaborative
approaches to improve care, improve health outcomes and
reduce care disparities in low-income, underserved populations in
Camden, New Jersey. This approach will have implications for the
commercial models of drug companies. Successfully
launching products in such networks will require
an altogether different focus on understanding and
articulating the value proposition to a community of
patients and the network of participants.
Building a network of this magnitude, and with this
much disruptive potential, is no trivial task. For
organizations interested in moving in this direction,
a good starting point might be to create disease
networks which leverage the creative models that
many health care systems are now piloting — from
accountable care organizations and patient-centered medical
homes in the US to primary care trusts in the UK. By focusing
on outcomes, patient-centric approaches and preventive care,
such programs already provide some of the key building blocks
of a HOLNet approach. HOLNets could supplement such models
by bringing a broader spectrum of constituents from across the
ecosystem. They could also bring a disease-specific focus and,
more important, create a bold collective intent to cure or radically
improve outcomes within that disease.
At Ernst & Young, we are actively engaging with a broad
spectrum of health care stakeholders to build CICNs. People
see the need for change and recognize the tremendous
transformative potential of a holistic network approach.
Getting there won’t be easy, but we’re moving in the right
direction. Stay tuned.
Health care
delivery
transformation
• Improved outcomes
• Patient-centric
approaches
• Patients for life
• Prevention
• Community-based
approaches
R&D transformation
• Pooled precompetitive data
• Standards
• Real-time learning
• Adaptive trial
designs
Commercial
transformation
• Pills+
• Services/solutions
• Outcomes-focused and patient-centric
• Demonstrating value with ecosystem data
PatientN
Self-managed
patient
Payers Careg
iversFamily
Communities
Physicians

Despite these pressures, pharma companies are acutely aware
that more needs to be done to sustain the ecosystem of innovative
emerging companies — not least because their own future growth
depends on it. This is a subject that large companies are giving
serious consideration. How can they do more to boost R&D
productivity and support the ecosystem of emerging companies at a
time when their own resources are growing relatively constrained?
One solution that has been proposed by some industry veterans
is that pharma companies should band together to create a
fund to purchase biotech IPOs. In the 9 January 2012 issue
of BioCentury, for instance, Moncef Slaoui, John Maraganore
and Stelios Papadopoulos argue that such an approach could
validate companies and their approaches for other investors
and give a boost to the market for biotech public offerings. (For
more on this approach, see the article by Moncef Slaoui on page
21; John Maraganore’s views on making R&D more sustainable
and productive can be found on page 17.) While this is certainly
an innovative idea that might be worth trying (assuming that
governance and other challenges could be appropriately
addressed), it is not clear that catalyzing several more IPOs every
year would be sufficient to truly address the strains on biotech
funding and the overall drug innovation model.
More important, pharmaceutical companies have much more to
offer than just financial capital. Even more valuable than funding are
the other assets that pharma could contribute — data, knowledge
(including valuable lessons about what has not worked) and human
capital. If each large pharma shared some of these assets and
allocated a tiny fraction of what it spends each year on in-house
R&D to set up a HOLNet with other constituents around a particular
area of interest, it might well have more impact on the efficiency
and economic return of drug development than a business-as-usual
approach. Pharma companies that take the lead in establishing
HOLNets could also benefit by attracting the most innovative
biotech and academic collaborators. This would represent a clear
departure from the current business development model, which
is focused on securing technology/product rights and maintaining
control of key decisions and data.
In Progressions, we talk about the challenge that pharma companies
face when trying to serve as “aggregators” in their experiments
with outcomes-focused approaches and partnerships. Pharma
companies are often viewed with suspicion in these coordinating
roles because they are perceived to have several conflicts of
interest in the outcomes business (e.g., increasing the use of
generics and focusing on prevention could save health systems
large sums of money but would cannibalize pharma product
sales). But HOLNets are an area where they could play a central
role in developing a new business model, while establishing their
credibility by contributing their own assets, bringing together a wide
range of participants (including, where appropriate, competitors)
and setting up rules for open sharing and access. Unlike their
experiments with outcomes-focused business models, this would
be much closer to their traditional business of drug development.
It would also be consistent with their corporate missions’ focus on
bringing meaningful new medicines to patients. And the payoff
could be bigger: a way to truly jump-start innovation, accelerate
the development of new products and improve health care delivery.
By embracing and developing HOLNets, pharma companies would
be helping themselves, helping the ecosystem of emerging biotech
companies and, ultimately, helping patients.
Pharmaceutical companies have much more to offer
than just their ﬁnancial capital.

Biotech companies and investors
As we’ve been discussing in these
pages for the last few years, the biggest
challenge for biotech companies and
their investors is sustaining innovation at
a time when the long-standing business
model for investment and R&D is under
unprecedented strain. Sustaining
innovation will inevitably involve some
combination of drastically reducing
development costs and time frames on the
one hand and significantly boosting pipeline
output on the other.
These pressures have led to much soul-
searching by biotech leaders and investors.
Already, we have seen challenges to
long-established ways of operating and
increasingly creative approaches to
partnering, financing and conducting R&D
that attempt to adjust the risk/reward
equation. Yet, these initiatives are not
enough — even in aggregate — to truly make
the biotech innovation model sustainable
and fuel the leaps in productivity and
efficiency that are needed.
Against this backdrop of challenges,
companies and investors are more likely
to be receptive to new approaches than
at any point in the industry’s past, and
HOLNets have many advantages for
biotech companies as well. As part of
such a consortium, biotech firms could
contribute their own innovative strengths
but, importantly, would also gain insights
from other members, including into
previously unsuccessful approaches for
target selection, clinical trial design and
the like. In addition, biotech firms may
gain access to regulators in a way that an
individual company would be unlikely to
achieve — in effect getting an early view at
new standards as they are being developed
and even playing a role in shaping them.
Getting personal, getting networked
Christian Itin, PhD
Micromet, Former President, CEO and Director
As demographic trends and increasing prosperity increase health care costs, innovators
need solutions that truly address underserved medical needs while also reducing
overall costs. To do this, constituents across health care will need to avoid unnecessary
treatments — making personalized medicine approaches increasingly relevant. Yet,
biotech and pharma companies face several challenges in accomplishing this goal.
For most diseases, we lack diagnostic markers for selecting appropriate patients. The
economics are challenging, with smaller market segments, clinical trials and ongoing
post-approval commitments to ensure safety.
To truly achieve the potential of personalized medicine, we will need more
collaboration. We are in early days of identifying molecular biomarkers correlated
with disease progression and outcomes. Today, the search for biomarkers in clinical
trials and development of companion diagnostics is done by individual companies.
But to really succeed, we need larger databases and uniform standards. Creating
this knowledge base systematically for all key disease areas is a huge undertaking in
terms of scope, time and resources and can only be tackled through a broad common
effort. Payers, pharmaceutical companies, regulators and clinicians have a common
interest and will need to work together to generate such data sets, building on
initiatives under way in the US and Europe. Policy makers may need to create stronger
incentives for biomarker studies. We will need strong protections for patients’ privacy
and other rights. It will be critical to get a broad spectrum of entities to join these
networked efforts and we will need to negotiate access to their data — from the massive
claims databases of payers to R&D data developed by the life sciences industry and
government. Pooling such data and making it publicly available would provide a key
starting point for new innovations in diagnostics, therapeutics and patient care.
For small companies in particular,
such access is both hard to come by
and increasingly valuable at a time of
heightened regulatory uncertainty and risk.
For small companies, participation will
likely be a trade-off between the perceived
need to hold on to information and the
benefits of participation, such as access
to regulators and earlier insight into
new standards before they are publicly
disclosed. As Marc Cantillon and Magali
Haas articulate in their articles, early
examples of open learning networks have
had no problem attracting small companies.
In an environment where the existing
model is under strain and companies and
investors are looking for ways to reduce
the regulatory and other risks associated
with drug development, we think that
others may similarly see a net benefit in
participating. Over time, if this approach
gains traction and shifts the very paradigm
of drug development, companies may find
that investors see participation in a HOLNet
as a significant risk-reduction strategy.

Providers
For providers, a key challenge in the new ecosystem will be
figuring out how to succeed in an outcomes-driven, patient-centric
world. In the US, physicians will increasingly find themselves
moving from a fee-for-service model to one in which they are
rewarded based on episodes of care or their ability to improve
outcomes. Indeed, the emphasis on outcomes is likely to affect
providers everywhere, as payers across the world look at ways to
manage costs.
To succeed in this environment, providers will need to improve
patient outcomes — and to do that, they will invariably need to
get closer to patients. While one could argue that providers are
already closer to patients than most other potential HOLNet
members, the interactions they currently have with patients are
a far cry from what success will increasingly require — enduring
relationships and a deep understanding of individuals’ needs,
conditions and behaviors. Health care delivery will need to move
from a world in which patients only meet their doctors sporadically
— typically for an annual checkup or when they fall sick — to one
in which new technologies and more sophisticated data allow
providers to monitor patients’ conditions on an ongoing basis and
develop real-time insights into the progression of their diseases.
This is one reason we are seeing an acceleration in EHR adoption
and the use of data to better define standards of care. Over time,
providers will also need to develop enduring relationships with
patients to truly provide holistic care.
Providers would have much to contribute — EHR data, patients for
clinical trials, etc. — and would also gain much in return, including
the ability to improve outcomes by learning in real time from
research and from a richer pool of data that makes connections
between EHRs, genetic profiles, claims data and much else.
Payers and policy makers
The interests of payers and policy makers, perhaps more than
those of any other entities, are perfectly aligned with the move to
HOLNets. Indeed, the changes they are making to incentives to
address their biggest challenges — the need to tame health care costs
while simultaneously covering more unmet medical needs due to
demographic changes and expansions in coverage — are accelerating
the shift.
As already discussed, these entities have so far been addressing
these challenges by moving toward outcomes-based models such
as adopting some form of health technology assessment and
negotiating pay-for-performance or episode-of-care reimbursement
arrangements. With HOLNets, they have the opportunity to take
this to the next level. Payers might contribute claims data and would
benefit significantly if these networks are able to drive down costs
across the spectrum of health care. Additionally, HOLNets provide an
opportunity to increase the focus on developing cures for diseases
where there is significant unmet social need — a big gap between
the costs a disease imposes on society and the resources currently
devoted to R&D. It is no coincidence that early examples of this
approach are often focusing on diseases where this gap is large, such
as Alzheimer’s disease and Parkinson’s disease.
Non-traditional entrants
The move to an outcomes-focused ecosystem is attracting a host of
“non-traditional” entrants. Firms from a broad range of industries —
information technology, telecommunications, retail trade and others
— are drawn by the opportunity to apply their skills to the challenge
of making health care costs sustainable. Developing new offerings
that are patient-centric and outcomes-driven will involve combining a
wide variety of capabilities. And at a time when finding new sources
of growth is often challenging, the sheer size of the opportunity in
health care is an attractive target. It will often be necessary to include
some of these companies in HOLNets, since the skills and assets
they bring (e.g., data mining, analytics, mobile technology to interact
with patients) could be very valuable. They might participate as full
partners or on a more limited, fee-for-service basis.

Patients and disease foundations
Last, but certainly not least, patients will
need to be part of the HOLNet approach.
Indeed, as already discussed, patients are
at the center of the new ecosystem, with
more control over their data and health
care. Certainly, they have much to gain
by participating — no one has a bigger
interest in improving health outcomes than
patients themselves. And, as discussed
above, HOLNets are often likely to focus
on intractable diseases where there are
significant unmet needs — something that
patients in those disease groups should be
happy to encourage, particularly at a time
when there is increased competition for
relatively scarce R&D budgets.
To make this happen, patients will need to
contribute their data — genetic information,
social media threads, data about their
conditions, disease progression, side
effects, etc. As we move to a world where
patients have more control over their
data, it will be important for HOLNets and
their member entities to be transparent
about how this data will be used, clearly
articulate the benefits to patients and
ensure compliance with their data usage
policies. Privacy remains a sensitive issue —
particularly in an area as personal as health
— but with appropriate protections (such
as separating medical data from personally
identifiable information), informed consent
and a full understanding of the benefits,
patients can be motivated to participate. As
“trusted brokers,” disease foundations could
help encourage patient participation.
So far, disease foundations have led the
charge on behalf of patients by driving
academic researchers, companies and
regulators to focus on the urgent needs of
patients with a particular condition. These
organizations will continue to play a critical
role, since they have the trust of patients
and can serve as an important intermediary.
In some cases, however, it may also become
imperative to broaden the focus beyond
individual diseases as we currently define
them. We turn to this aspect next.
Partnering for specialization
John Maraganore, PhD
Alnylam Pharmaceuticals, CEO
If big pharma’s pipelines are any indication, the drug industry is
starving for innovation. While pharma needs to access biotech’s
innovation, biotechs and their investors are resistant to cede
its value. This is a recipe for stalemate. The industry needs
different partnership structures which fully pay biotech firms for
innovation and leave early-stage development in their hands,
while allowing pharma to conduct later-stage development and
commercialization.
The question is whether pharma can rely fully on biotech for its
innovation and whether biotech can rely fully on pharma for its
late-stage development and global commercialization. For too
long, the industry has been splintered into camps, each focused
on building value within its own organizations the only way it
knows: by maintaining control over the entire value chain. But
drug R&D and commercialization are now far too complex for
any one company to be good at all of those disparate activities.
The solution is increasingly obvious: partnership structures
in which the discovery innovator gets paid for, and maintains
control of, higher-risk stages — and where the pharma partner
provides downstream expertise. But this solution in turn requires
a fundamental rethinking of transaction and company structure.
No biotech will forfeit the value of its innovation without a
considerable rethink of the economics. Nor will it readily give
up control of early development, for fear of being buried in a
large company’s bureaucracy or sidelined in favor of an in-house
candidate. Only when companies cede authority over those
areas in which they aren’t competitive will we see an industry
whose productivity is commensurate with its investment, an
industry best prepared to harness the remarkable pace of
biomedical discovery and capable of meeting its obligations to
patients.
It may become imperative
to broaden the focus beyond
individual diseases as we
currently deﬁne them.

Beyond disease
It is not surprising that many of the early examples of R&D networks are focused on
brain diseases. After all, these are conditions where there is a large (and, thanks to aging
populations, growing) unmet medical need coupled with insufficient R&D investment
relative to the cost imposed on society. The brain is perhaps the most complex system in the
human body, but because of our limited ability to access this organ, it is also one of the least
understood. These challenges have made it exceedingly difficult to develop treatments in
this area — leading investors and companies to pull back because of the high risk involved.
While this investment gap — between the societal cost and level of investment — might be
most significant in brain diseases, similar gaps exist for other ailments. Chronic diseases,
for instance, are expected to impose a very large and rapidly escalating societal cost as
populations age and emerging markets grow increasingly prosperous. While we have proven
drugs to manage these conditions, very little has been done to apply personalized medicine
approaches to better classify these diseases into subtypes and develop treatments that are
more targeted and efficacious. At the same time, the economics of developing drugs for
these diseases has become increasingly difficult, as drug developers have to compete with
newly generic versions of their own past successes and an exceedingly cautious regulatory
environment has escalated safety concerns in these indications. Chronic diseases would
therefore be another prime candidate for a HOLNet approach, to close the gap between the
high societal cost/medical need and relatively low levels of investment.
It is equally noteworthy that most of the efforts to close such gaps are being led by
disease foundations and other nonprofits. Yet, HOLNets may at times also need to rethink
traditional boundaries and definitions of disease. This has already been happening in
personalized medicine, as insights from genetic data have redirected how we think about
disease. In cancer — the area where personalized medicine approaches have made greatest
headway — it has become increasingly apparent that what’s relevant is not where the
disease is manifested (“breast cancer” or “blood cancer”) but the mechanism that causes
it (e.g., a specific genetic mutation). By the same token, a disease foundation focused on a
particular type of cancer may be too narrow if it seeks to develop a HOLNet purely for this
ailment. It is therefore appropriate that organizations such as One Mind for Research are
focusing on all brain diseases holistically rather than focusing only on individual diseases
such as Alzheimer’s or Parkinson’s. Over time, such groups may find that even a widening of
disease boundaries is too limiting an approach. After all, the human body is a very complex
and interconnected system.
The bottom line is that, while many of the early examples are being led by disease
foundations, it will be imperative to ensure that the focus is not overly narrow. Today’s
networks have often been referred to as “disease networks,” but we think this name is too
narrow and have intentionally chosen a broader term to emphasize that what’s important
is not the focus on a specific disease but the creation of a framework that allows for
continuous learning from real-time information sharing and openness.

Conclusion
Today, the health care ecosystem and its constituents face historic challenges. At a time
when key stakeholders — payers, pharma companies, biotech firms and their investors —
are increasingly resource-constrained, we need R&D paradigms that are several shades
more efficient and productive. With aging populations and rapidly growing middle classes
in emerging markets, societies need ways to accelerate cures for ailments that are
expected to impose huge societal costs, such as neurodegenerative and chronic conditions.
And as health care moves to a new patient-centric, outcomes-focused ecosystem, its
constituents need ways to develop deeper relationships with patients to demonstrably
improve their outcomes.
HOLNets provide some answers to all of these challenges. At a time when traditional
approaches have become increasingly untenable, the HOLNet is a boldly different paradigm
that seizes the opportunities latent in the changing health care ecosystem — big data, real-
time insights, the diverse strengths of a wide range of players.
Getting there will take some adjustments. If HOLNets are about openness and learning,
health care’s constituents will often need to be open to new approaches and learn new
ways of doing things. For life sciences companies, this will involve different ways of thinking
about intellectual property and recognizing that in some situations, sharing information
may create more value than protecting it. Regulators will need to adapt frameworks to allow
for drug development paradigms that are flexible and learn in real time. And ultimately,
patients will need to willingly share their personal health data, with the recognition that
they might reap some of the biggest dividends from this approach: better health outcomes,
better drugs and cures for long-intractable diseases.
The HOLNet is a boldly different paradigm that seizes the opportunities
latent in the changing health care ecosystem — big data, real-time
insights, the diverse strengths of a wide range of players.

Patient-centric innovation:
networked and personalized
N. Anthony Coles, MD
Onyx Pharmaceuticals
President, CEO and Member of the Board
Over the past decade, the traditional “one-size-fits-all,” chemistry-
based approach to pharmaceutical drug development has become
increasingly untenable. It now takes an average of more than
US$1 billion and 12 years to bring new products to patients, and
only 10% of promising compounds become new medicines. Spurred
by the need to make drug development more cost-effective and by
advances in genomics and genetics, a new paradigm has emerged
that balances traditional chemical approaches with biological
and genomic techniques to identify a new generation of targeted
therapies. These “smart drugs” can be given to the right patient at
the right time and can treat the individual basis of disease.
Meanwhile, our industry’s long-standing “go-it-alone” approach, in
which companies attempt to single-handedly discover and develop
new medicines, is being challenged for its scientific productivity
and efficiency, and for the absence of scale in an age of rapid
innovation. We have to — and are starting to — find new ways to
accelerate the development of even better therapies for unmet
medical needs. Underpinning this new approach is the opportunity
to collaborate with key groups and stakeholders to, in effect,
“network” innovation. By partnering with networks of academicians,
scientists, regulators, policy makers and patient communities, we
can create more breakthroughs that patients desperately need.
First, and most important, our focus must remain on the patient.
As clinicians, we understand that no two patients are alike. For
industry to embrace the individual differences between patients,
we will need an intense focus on personalized medicine. By taking a
holistic, patient-centered approach and integrating patients’ genetic
information with genomic research, we can partner with patients
to move beyond one-size-fits-all pills and create new approaches to
personalized health. Access to electronic medical records, biometric
data and emerging technologies from the digital revolution leads to
the assimilation and utilization of state-of-the-art clinical knowledge,
which can allow us to move even closer to patients to meet their
needs.
At the same time, we must work even more closely with
governments and regulators around the world. It currently takes
too long to bring a new drug to market, particularly when one
considers the benefit and life extension many therapies provide.
Efforts are currently under way by both regulators and lawmakers,
in collaboration with industry, to shorten the time from bench to
bedside. But more work must be done to incorporate the latest
thinking about new clinical trial approaches and the acceptable
trade-offs between risk and benefit in order to race forward with
much-needed improvements to our existing crop of therapies.
Despite the progress we have made in several disease areas, several
others — cancer, Parkinson’s disease and Alzheimer’s disease, to
name a few — still need new and effective treatments.
Finally, we must be creative and open-minded about new
partnerships — with other companies, academic institutions,
government and nonprofit organizations — and need to move
beyond the standard technology licensing approach. This means
more unique collaborations, such as the one between Ford Motors
and Medtronic to create an in-car glucose monitor, or between
Novartis and Nintendo to raise disease awareness and educate
patients through the use of online gaming. In an effort to create one
of these new approaches, Onyx has recently initiated an innovative
research alliance with the University of Texas MD Anderson
Cancer Center to accelerate the discovery and translation of new
knowledge about cancer from the laboratory to patients.
The biopharmaceutical environment grows ever more complex
and potentially more prolific as new technologies give us fresh
insights into biology and the genome and as new digital tools make
it possible for researchers, patients and providers to collaborate
in ways that would have been impossible even a decade ago. This
fundamental shift in thinking and activity creates the potential for
tremendous upheaval, and with this disruption comes unparalleled
opportunity to experiment with new models of collaboration. With
so many people pulling toward a common goal, our challenge now is
to all pull in the same direction.

21Moncef Slaoui Protecting the biotech ecosystem
Protecting the biotech ecosystem
Moncef Slaoui, PhD
GlaxoSmithKline
Chairman, Research & Development
At GlaxoSmithKline, we have about 14,000 scientists and spend
almost US$6.4 billion a year on R&D. With such vast resources, one
could easily believe — and many large companies did as recently as a
decade ago — that having a healthy ecosystem of emerging biotech
companies is not terribly material to our success.
In fact, we believe the opposite. We have 14,000 scientists, but
there are probably a million life science scientists in the world —
which suggests that we will generate only 0.1% of the good ideas.
So, everything we do is built on the premise that we need a strong
ecosystem of biotech companies.
Yet, today this ecosystem is threatened. The biotech industry
has historically thrived because investors could earn high returns
commensurate with the huge risks involved in drug R&D. In recent
years, investors’ willingness to make such high-risk bets has
declined, for two reasons. First, as pharma companies’ resources
got squeezed, they started looking for lower-risk approaches and
investments. Second, returns from IPOs have declined — putting the
VC investment model under strain.
As a result, GSK is beefing up its venture arm, SR One. We have
also announced deals with at least three other VCs — Europe’s
Index Ventures, Boston’s Longwood Fund and North Carolina-
based Hatteras Venture Partners — where we are investing as
limited partners. And we continue to look for similar opportunities
elsewhere with the right VCs.
We are also very active in business development, with alliances
with about 50 different biotech companies. Almost all of these are
strategic in the sense that they are not focused on a single project
but rather on an entire segment of the company’s portfolio. This is
critical, because it creates multiple exit opportunities for investors.
Beyond these efforts, I’ve also proposed — with a couple of other
industry veterans, John Maraganore and Stelios Papadopoulos —
that pharma companies should consider creating investment funds
with the purpose of buying biotech IPOs. Acquisitions do not
currently represent a sustainable exit strategy, since they have very
high hurdles and are relatively infrequent events beyond the control
of small companies and their venture investors. Pharma-supported
investment funds could boost the IPO market by validating
companies — after all, pharma buyers have the most sophisticated
technical capabilities for assessing technology risks and the value
of companies.
Another way in which pharma companies could do more to support
the ecosystem is through precompetitive collaboration. In target
validation, for instance — the process of figuring out whether a
certain target can affect a particular biology and physiology for a
disease — about 60%–70% of the targets we are working on are also
being pursued by our competitors. This is expensive and wasteful,
because target validation is not where we ultimately compete. The
competitive play really comes after a target has been validated — for
instance, in the kinds of chemistry we develop to create a drug.
This is particularly relevant in neurodegenerative diseases such
as Alzheimer’s, where target validation is tremendously slow and
expensive for numerous reasons. Animal models have proven
ineffective, so validation has to happen in the clinic. Cognition is a
subjective measure, and you need very large patient populations
to truly understand it. Lastly, neurodegeneration is a very slow
process — it takes 10–20 years to express itself clinically.
Precompetitive collaboration may not be universally applicable.
In disease areas where target validation is fairly quick and
straightforward, companies may not have much incentive to
collaborate, and many biotech companies, in particular, view
target validation as a source of competitive advantage. But in
certain disease areas, precompetitive collaboration could be a
game changer.
Now, more than ever, we need game changers. Sustaining the
biotech ecosystem is not an act of charity or corporate social
responsibility — it is in the self-interest of big pharma companies.
The good news is that through approaches such as the ones
described here, we can use our extensive resources to really make
a difference.

To boost R&D, stop flying blind
and start observing
Joshua Boger, PhD
Vertex Pharmaceuticals
Founder and Board Director
Drug development is often described as a linear process:
formulating a hypothesis and then testing it in a series of
experiments. That’s accurate as far as it goes, but it minimizes
another pillar of science, observation. Science is an inherently
iterative process: hypotheses are refined based on observations
and learning from prior experiments. Yet in our industry, drug
development has become less and less about observation and
more and more about a rigid approach to hypothesis testing. This is
particularly true in Phase II clinical trials, the central — and arguably
most important — phase of the development process.
The purpose of Phase II is to identify and begin to frame a drug’s
possible benefits — how it improves health outcomes and the risks
it might carry — which involves observing benefits and risks and
assembling data to determine dosage amount and schedule. While
this definitely requires hypotheses based on previous observations,
the process of observation and hypothesis generation needs to
continue in Phase II, as well.
Unfortunately, today’s Phase II trials often pay lip service to
observation and exploration, breezily truncate dose selection
and do not welcome hypothesis generation — often before a drug
candidate’s effects and side effects have been well characterized. In
too many cases, we blow past experimental learning and go straight
to confirmation. So what’s wrong with that? Doesn’t that get you to
a drug sooner? Well, no, almost never.
In an era of scientific breakthroughs, almost every important new
drug will be forging new paradigms, breaking ground on endpoints
and mechanisms and may even be the first therapy targeting a
disease. At the start of Phase II, there may be some anecdotal
clinical observations and even considerable biochemical evidence
thought to be predictive of benefit, but all of these data are usually
based on assumptions and analogies with other approaches. One of
the many guarantees of clinical research is that you are going to be
surprised, with both downside and upside surprises. If you’ve locked
in your hypotheses before you get to Phase II, you’re going to miss
much of that upside, and you won’t be agile enough to cope with the
downside.
Over the last couple of decades, Phase II trials have often grown
from exploratory observational experiments at one or two clinical
centers to quite large “mini-Phase-III” trials at tens of clinical sites,
often on multiple continents, with tightly defined primary endpoints
and structures designed to obtain the holy grail: p-value. This
drive to obtain a p-value of “significance” (i.e., below the arbitrary
and religious cutoff of 5%) on a primary endpoint thought to be
“approvable” (i.e., acceptable for Phase III and for drug approval)
often leads to overreaching. In many cases, more modest endpoints
would be more appropriate for the state of the drug candidate and
the known data. The result is Phase II trials that are larger, longer
and more expensive than should be necessary to advance the drug
into Phase III.
Exacerbating the problem is the perceived need to “blind” Phase
II trials, keeping all but the most catastrophic observations under
wraps until the process is completed and the data locked. Ironically,
Phase II trials of this kind often generate a wealth of information
about rich secondary endpoints (in addition to the primary
endpoints) and about other scientific and mechanistic questions,
but none of these data are available for examination in real time,
due to the desire to “preserve the integrity” of the precious primary
endpoint and its p-value. This strict blinding of Phase II trials
imposes significant costs, including: lengthening the development
process; losing the ability to quickly incorporate lessons from the
trial into subsequent, even overlapping, trials; and losing the ability
to manage overall R&D resources in a more rational and timely
fashion. For sure, there are disease areas where, because of the
lamentable lack of objective endpoints, blinding of trials may be
required. But increasingly, efficacy endpoints for modern trials are
(or should be) beyond subjective influence. Safety reports, many of
which are self-reported by patients, might be unduly influenced by
inappropriate dissemination of ongoing data, but even here, there
are procedures available to minimize this bias.
So if running Phase II trials in a more open and exploratory manner
has so many possible advantages, who is against it? Why doesn’t it
happen more often? The interests of many constituencies maintain
this p-value-worshipping status quo. Investors and analysts crave
simplicity, and there is no simpler discriminator than “What’s the
p-value?” Complex, multi-endpoint, dose-range-finding trials are
punished on Wall Street. Investors ask, “Why are your trials so
hard to understand?” (The answer — that they were not designed
to be easy for investors to understand — is usually not welcomed.)
In addition, regulators often insist on p-value trials. In the last
couple of decades, the FDA has taken a far more directive role
in the design of Phase II trials. Ironically, as the opportunities for

2012 EY Biotech Report

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