Knowledge Capabilities and Economic Geography

Globalisation of Biosciences: Knowledge Capabilities and
Economic Geography
Philip Cooke
Centre for Advanced Studies & Centre for the Economic & Social Analysis
of Genomics (CESAGen)
Cardiff University
44-45 Park Place
Cardiff CF10 3BB
Wales (UK)
February 2004
Revised for Economic Geography January & March 2006
This paper builds on a suite of research studies examining the metamorphosis in industry
organisation, as Penrose calls it, regarding the centrality of firm capabilities in biosciences. Whereas
knowledge leadership capabilities used to be inside global corporations now they have given way to
university laboratories and dedicated biotechnology firm networks to access innovative research. The
basic argument is that research centre and small firm knowledge capabilities have generally
outstripped those of the multinationals in knowledge-intensive industry, a consequence of which is a
re-alignment in cause-and-effect outcomes shaping economic geography. This is particularly
pronounced in biosciences and pharmaceuticals. The paper mobilises a new theoretical framework
and new data that support the thesis that a realignment of industry organisation around knowledge
capabilities was pioneered in biosciences, is active in other industries, and in biosciences is now
entering a new phase. In Phase 1, large pharmaceuticals firms acquired equity in dedicated
biotechnology firms (DBFs) to access technological know-how. Failing to absorb this adequately,
they have now in Phase 2 become supplicants to the advanced basic research knowledge produced in
globally key university and other laboratories. These links are contextuated by biotechnology firms
that cluster in knowledgeable ‘network nodes’. Paying attention to global comparisons among
bioscience ‘network nodes’ we find that some European nodes in, especially, Sweden and UK are
performing well in international publication collaboration but less so in commercialising
innovations: a process of ‘decapitation’ being one consequence predictable from geographic
capabilities theory. On other measures such as numbers of firms and life scientists - European,
including Swedish, Swiss, German and UK centres perform fairly well against top North American
nodes. However the greater maturity of the latter still gives them advantage in commercial
exploitation of bioscience.
This paper presents an analysis of a new phase in globalisation viewed through the lens of medical
bioscience, widely recognised as the world’s most science-driven industry (see Cooke, 2005a).
Bioscience is the knowledge base connecting biotechnology to pharmaceuticals firms and ultimately
human healthcare. Of course there are other applications in agro-food, energy and environmental
control, but some 70% of bioscientific research and commercialisation activity is devoted to
healthcare through biopharmaceuticals and related industries that now extend into genomics and
post-genomic activities. The decoding of the human genome, mainly in the Sanger Institute,
Cambridge, UK, the Whitehead Institute, Cambridge, Massachusetts and Washington St Louis
University was expected to herald a burst of creativity in biopharmaceutical drugs and treatments
based in genomics. That this has not happened is a cause for recalling that transformation of
exploration into exploitation knowledge is not automatic. The millennium stock market downturn
made biotech investors nervous, the post-genomic transformation of decodes of large complex
proteins into prototype treatments is difficult and time-consuming, and knowledge transfer is slow.
This raises interesting questions regarding the issue of capabilities in biosciences, particularly
dynamic capabilities (Penrose, 1959; 1995; Teece & Pisano, 1996). Normally and historically
microeconomic theory would anticipate transformational capabilities of the kind to which postgenomics alludes to be resolved by the exercise of scale economies in large, probably
multinational corporations. However, numerous observers have commented on the fact that
pharmaceuticals corporations have shown declining, even vanishing capabilities, to move beyond
traditional prescription drugs produced using synthetic chemistry capabilities. Inevitably as
bioscientific knowledge accumulates and the scope for treatments discovered, many by accident,
under the synthetic chemistry regime suffer diminishing returns, the drugs ‘pipelines’ dry up. In
1996 53 new treatments were granted approval by the US Food & Drug Administration. In 2003
it was 21, and many of those were marginal variants upon existing products rather than wholly
new (Landers, 2004). There is thus something of a ‘capabilities gap’ between science based
research and dedicated biotechnology firms (DBFs) on the one hand, and large pharmaceuticals
firms (‘pharma’) on the other. Partly in response to the ‘capabilities gap’ a new phase of
globalisation involving the latter searching out the former in specific ‘capabilities niches’ not
only for acquisition but exploration (research) and exploitation (commercialisation) partnerships
has arisen. This paper seeks to display some of the global network patterns that are arising as the
two ‘epistemic communities’ (Haas, 1992) seek to bridge the gap. The terminology of ‘epistemic
communities’ is utilised here since it is widely understood that a key barrier to knowledge
capture within large pharmaceuticals firms is that, professionally, their expertise lies in
chemistry. Biotechnology is professionally aligned more with biosciences. Surprisingly, perhaps,
and despite such sub-fields as biochemistry and microbiology, the in-house development of
biotechnology products by pharmaceuticals firms has largely been a failure.
This is a remarkable condition for a global industry to find itself in, namely unable to master key
invention and innovation processes in its own core market capability of drug development.
Perforce, therefore to survive, ‘pharma’ has for decades been a wealthy ‘supplicant’ to relatively
impoverished small and medium-sized biotechnology businesses, themselves in most cases
clustered near leading bioscientific laboratories, research institutes and university centres of
excellence. It was thought for a long time that R&D outsourcing was largely confined to the
pharmaceuticals industry, but it is not. The oil industry spends directly only 0.4% of GDP on
R&D, buying it as needed from specialist project management firms like Halliburton and
Schlumberger, while since the 1990s there has been a transformation, notably in the US, in where
all kinds of industrial R&D is performed. The most notable statistic in the US National Science
Foundation’s analysis of this is that while in 1981 more than 70% of US industrial R&D was
conducted in firms employing more than 25,000 workers, by 2001 it was down to less than 40%.
The fastest R&D growth rate was registered by firms in the 50-249 employment range, where it
more than doubled (NSF, 2005). Why this major shift? In pharmaceuticals it is, as noted, an issue
of capabilities. According to Chesbrough (2003) in ICT it is a matter of corporate strategy to buy
R&D more than make it, either because of capabilities problems (e.g. Microsoft compared to
Linux; Raymond, 2001) or to cut wasteful expenditure by simply making acquisitions (Cisco
Systems model).
The paper proceeds as follows. There are three main sections, the first of which discusses
important theoretical issues surrounding the relations in functional and geographical proximity,
that arise in the metamorphosed circumstances where ‘open systems architectures’ are revalued in
circumstances where industrial ‘secrecy’ would once have prevailed (Best, 2001; Lambert, 2003;
Chesbrough, 2003). On the latter, consider the following quote from the UK government’s report
on university-industry interaction:
‘Two broad trends are reshaping the way that companies are undertaking research around
the world. The first is that they are moving away from a system in which most of their
R&D was done in their own laboratories, preferably in secret, to one in which they are
actively seeking to collaborate with others in a new form of open innovation. The second is
that business R&D is going global’ (Lambert, 2003, 3).
Compare this with the following quote from Penrose (1995):
‘...the rapid and intricate evolution of modern technology often makes it
necessary for firms in related areas around the world to be closely in
touch with developments in the research and innovation of firms in many centres’
(Penrose, 1995, p. xix)
The concept she says she would have used to denote this shift had it been available in 1959, when
The Theory of the Growth of the Firm was first published, is that of knowledge networks. These
transform the basic capability of the firm, namely that it is a repository of knowledge, mainly
knowledge of the core business and its administration, into a node in a global network of such
capabilities. Following this section is one that mobilises data of various kinds, both primary and
secondary, to assess the nature and extent of the change in industry organisation presaged by the
rise in prominence of scientific knowledge capabilities, notably in biosciences for university labs,
DBFs and ‘pharma’. Finally, in the limited space available an effort is made to show how
dominant tendencies to connect to the few global ‘megacentres’ of knowledge capability are also
contextuated by counter-tendencies among global niche actors. Few of these actual or aspiring
‘megacentres’ are so-called global cities, though a few are globally significant bioscientific
knowledge ‘transceivers’. This leads to some brief reflections upon the power of geographies of
knowledge to determine industry organisation, something of a historic reversal at least for this
sector, where corporate divisions of labour have tended to structure economic space.
The Rise of Global Knowledge Networks in Biosciences: Theory and Preliminary Empirics
In this section, it is shown how knowledge hegemony in pharmaceuticals has shifted to universities
and the cohorts of biotechnology firms that often co-locate with those that are ‘ahead of the curve’ in
biosciences. In the course of this account, a possibly new theoretical framework for explaining
spatial industry organisational shifts has to be essayed. It is evolutionary in origin, interested in the
economics of search and selection practices of firms in contexts where variety acts as ‘evolutionary
fuel’ in Hodgson’s vivid phraseology (Hodgson, 1993). By evolutionary fuel is meant iterative, trialand-error interactive feedback from experimentation by actors to survive and prosper economically.
The greater the variety, the greater the opportunity for innovation arising from interactions with other
actors. It has been shown empirically that opportunities for the swiftest innovation occur in
conditions of proximate and related variety (Boschma, 2005). Cities are one variant of this, but
because their variety is often fragmented as well as partly related, they are less fruitful than settings
with only related variety (e.g. clusters). Hence this new perspective settles at the apex of a
conceptual triangle between Jacobs (1969) who advocated sectoral diversification and Glaeser et al.
(1992) specialisation as key wellsprings of innovative growth. It is post-sectoral, recognising
innovative growth to be facilitated through knowledge or technology platforms characterised by
openness of knowledge flows. For example, a location specialising in leading edge research in
sensors finds numerous applications of such technology in many related yet extensive fields where
absorptive capacity is high. A priori biotechnology is the exemplar of this mode of industry
organisation, but more as pioneer than offshoot now that the model of ‘open innovation’ building on
‘open science’ norms is emulated widely, from ‘open systems architectures’ of various kinds to
‘open source’ software (Owen-Smith & Powell, 2004). The point here is that ‘open science’ norms
among scientists operate informally through normal ‘channels’ even if ‘formally’ confidentiality
agreements exist with clients. As Chesbrough (2003) notes clients know this – complicitly - in the
knowledge also that they will themselves receive returns from localised knowledge spillovers in the
cluster. Not all of this openness is geographically proximate, distant networks play a strategic part,
and cognitive and relational proximities come into play as Boschma (2005) stresses.
Nevertheless, the implications of related variety as witnessed in the demise of the generic corporate
R&D model compared to the varietal choice model found in the rich mix of research centres and
niche firms in, for example a major biotechnology cluster, is testimony to the attractions to
customers of the latter over the former model. These may be measured in terms of capabilities
ranging from those relevant to exploration, examination or exploitation knowledge (Cooke, 2005b).
Hence, there are grounds for advancing a theoretical framework that links together these new elements and
highlights the role of varieties of knowledge in contributing a testable explanation of regional
developmental asymmetries. The key elements are presented in Fig. 1 below and discussed subsequently.
(to Variety)
Regional Knowledge
(e.g. Epistemic
(e.g. Clusters)
(Raises Outsourcing)
(Raises Absorptive
Fig. 1: Knowledge Capabilities and Economic Geography: A Theoretical Framework
We start from the centre of the diagram, denoting a region in which a mix of widely in-demand
knowledge capabilities evolves. Connecting to north-west in the diagram, and compared to other
regions, this expresses its asymmetric knowledge endowment from a variety of knowledge
organisations and institutions. Exploration knowledge organisations, such as research institutes,
knowledge networks among individuals (e.g. ‘lunar societies’; Uglow, 2003) and knowledge
leadership figures (e.g. possible future Nobel [or Oscar] laureates) co-exist with examination
knowledge equivalents for standard-setting, trialling, testing and patenting, and exploitation
knowledge bodies such as entrepreneurs, investors and related professional talent. The evolutionary
fuel is supplied (linking westward in Fig. 1) by the attraction of a variety of imitative and innovative
talent to the region, a Schumpeterian ‘swarming’ realising increasing returns to related variety
(south-eastward diagrammatic connection) where innovation may move swiftly through various parts
of the innovation ‘platform’. Related variety nourishes absorptive capacity because cognitive
distance between platform sub-fields is low (think of ‘general purpose innovations’, after Helpman,
Moving north-east in Fig. 1, these processes result in the presence of regional ‘knowledge domains.’
The dictionary definition of ‘knowledge domain’ is a region or realm with a distinctive knowledge
base, common principles, rules and procedures, and a specific semantic discourse. This naturally fits
well with the concept of the epistemic community with its own professional discourse and interests.
Such monopolistic features are frequently characteristic of, for example, clusters that in regional
terms may display related variety (e.g. varieties of engineering expertise in the industrial districts of
Emilia-Romagna in Italy in a spectrum from Ferrari cars and Ducati motor cycles (both Modena) to
Sasib in packaging machinery (Bologna) and drgSystems machine tools in Piacenza; Harrison,
1994). These and other clusters have spatial quasi-monopolistic or ‘club’ characteristics, exerting
exclusion and inclusion mechanisms to aspirant ‘members’ consequent upon their knowledge value
to the club. If such industries operated as markets rather than knowledge quasi-monopolies it is
difficult to see why spatial ‘swarming’ would occur. But high technology firms at least are willing to
pay super-rents of 100% to locate in clusters – even when they are professed non-collaborators, to
access anticipated localised knowledge spillovers (Cooke, 2006). Finally, to the south-west of Fig. 1,
it is precisely such localised knowledge spillovers that induce what Chesbrough (2003) calls ‘open
innovation’ whereby large firms outsource their R&D to purchase ‘pipeline’ knowledge, and access
via ‘channels’ regional knowledge capabilities (Owen-Smith & Powell, 2004). These processes
interact in complex, non-linear ways displayed graphically in Fig. 1, to explain regional knowledge
asymmetries. Variations in the market value of regional knowledge combinations also contribute
significantly to associated regional income disparities (Boschma & Frenken, 2003). Being an
evolutionary growth process, successive increasing returns may be triggered from any point within
or, of course, beyond the confines of Fig. 1.
To further exemplify this, an interesting and important shift in the organisation and location of
knowledge exploration (basic research) and exploitation (commercialisation) in recent years has been
the rise of the entrepreneurial university and the demise of in-house corporate R&D. In Table 1,
figures are provided showing shifts in scale and focus of royalties from patents licensed to outside
users from US universities 1993-2002. It is instructive to note three key things about the royalty data
noted in the annual surveys, here represented by three years only. First, there was a remarkable
increase in scale of royalty income earned by the entrants in the top ten placements. The top royalty
earner in 2002, Columbia University earned ten times the income that Wisconsin had in 1993.
Second, there was a significant shift in the institutions performing well in 2002 compared with 1993.
The earlier successes like Wisconsin and Michigan State had only doubled their royalty income over
the period, while Columbia, NYU and Stanford, for example entered the higher reaches later, not
Rank University Income
Rank University Income Rank University Income
Columbia 155.6
New York U. 62.7
Florida S.
U. of Florida 31. 6
Michigan S. 29.8
Emory U.
Florida S.
Michigan S.
U. of Florida
Carnegie M.
Yale U.
U. Washington14.8
Michigan S. 14.1
Iowa State
U. of Florida 5.7
U. of Virginia 3.5
U. Michigan 1.2
U. Minnesota 1.1
Table 1: Royalty Income Top 10 US Universities (Public & Private)
NB: University System Data Excluded
Source: Association of University Technology Managers Inc. Licensing Survey, Chronicle of Higher
Education, December 2003.
even appearing in the top 25 as recently as 1995. This suggests the onset of a surge in income
streams after 1996. Finally the loss of seven 1993 top ten entrants by 2002 with four not even in
2002’s top twenty again suggests a shuffling in nature and scale of revenue among top royalty
earners. One hypothesis is that, by 2002 bioscientific licensing royalties had appeared, some with
‘blockbuster’ status for the successful incumbents.
Inspection of quantitative data on three of the top five entrants in 2002, and expert comment (Agres,
2003) reveals this hypothesis to be valid. Columbia’s lead patent, describes a system for
manufacturing complex proteins by inserting genes into mammalian cells. More than half the
University’s patents are for pharmaceuticals or other medical technologies developed at Columbia’s
College of Physicians & Surgeons. However, the perils of the new entrepreneurship were captured in
the following report in July 2003 that biotechnology companies Biogen, Genzyme and Abbott
Bioresearch Centre had filed a joint lawsuit against Columbia University in an attempt to invalidate a
gene-splicing technology patent held by Columbia. The complaint accuses the university of an
illegitimate effort to create a ‘patent monopoly’, placing hundreds of millions of dollars in potential
royalties and license fees at stake. Other biotechnology companies, including Californian firms
Amgen and Genentech had both filed similar suits earlier that year. Over the life of the original
patents, Columbia received an estimated $300 million in licensing fees and royalties (Howard,
Despite this, Columbia University clearly led the field, with the vast majority coming from pharmaceutical
patents. Indeed, most of the $511 million amassed by the high scoring US universities was derived from
life sciences-related discoveries (Agres, 2003). For Florida State University, there was an enormous single
royalty cash flow from Bristol Myers Squibb for the Taxol® manufacturing process. This drug is the
world’s best-selling anti-cancer treatment. While the cashflow is spoken for, the interest earned from
investing it is combined with overhead from non-government contracts and grants, and used to fund the
technology transfer office and the contracts office personnel which handle non-government contracts and
grants. At Stanford University the royalty-sharing policy distributes royalty cash net (15% having been
deducted to fund the Technology Liaison Office) to inventors, their departments and schools. In 2000
inventors received $5.9 million, their departments $6.9 million and schools $6.9 million. The Stanford
School of Medicine received $4.4 million, approximately four times the scale received by the next
recipient. This indicates the dominance of medical biosciences as a generator of royalty income from
patenting at Stanford (Office of Technology Liaison, 2000).
Of course, these are not large sums compared to those paid by ‘big pharma’ to other organisations,
including DBFs. Thus the US federal government’s National Institutes of Health spent $183 million to
research the aforementioned Taxol, the best-selling cancer drug in history with revenue of $9 billion. But it
recouped only $35 million of that cost in licensing deals with Bristol-Myers Squibb to concern from the
US Senate at inadequate efforts by the National Institutes of Health to recoup taxpayers' investment in
basic medical research. However, the NIH's contention is that it lacked the bargaining power to negotiate
more successfully with Bristol-Myers. There were only four bidders for the license and Bristol-Myers was
adjudged substantially more qualified to commercialise Taxol, especially given NIH rules that prioritise
swiftly transforming research for patient care above maximizing royalties. In 1991 NIH signed a
cooperative R&D agreement with Bristol-Myers requiring the company to develop a method to
manufacture Taxol with no call for royalties. Bristol-Myers, in collaboration with Florida State University
launched the drug on the market in 1993. In 1996, Bristol-Myers agreed to pay royalties of 0.5% on
Taxol's worldwide sales in exchange for licensing three NIH patents on methods for using Taxol in cancer
treatment. These have yet to be activated, indicating ethical and market capability dissonances.
Clearly, reliance on DBF knowledge capabilities is the most likely means by which what some have
labelled ‘corporate dinosaurs’ to survive, although their deep pockets will keep them active as financiers,
and marketers of new drug candidates that they may, through business networks, orchestrate into being. A
view that this perspective on the possible demise of ‘big pharma’ is overstated is, however, presented
articulately by Paul Nightingale. In a lengthy paper he argued the following. The change from synthetic
chemistry to biology as the epistemological basis for pharmaceuticals companies had brought to the fore
genetics, database, screening and bioinformatics technologies allowing pharmaceuticals firms to utilise
their natural economies of scale in experimentation if not R&D. The automated, mass-production analysis
of patients and ‘in silico’ simulations using large databases to mobilise the combinatorial chemistry
required to identify compounds that act as molecular disease-inhibitors gave them an optimistic future. The
main reason concerned time-economies and ‘throughput’ capabilities that only the administrative
capabilities of scale could satisfactorily master (Nightingale, 2000).
Intriguing, therefore, to read the following analysis that re-directs our attention from the failings of ‘big
pharma’ in basic research, resulting on their heavily increased reliance upon DBF capabilities, to newly
emergent failures in precisely the capabilities identified by Nightingale and others as those of ‘scale’ that
would triumph in the face of the DBF challenge. Traditionally many pharmaceutical companies had large
industrial-chemical or consumer-products businesses to smooth out the cycles of drug research. The
industry shed those businesses over the years to focus on prescription medicines, which brought higher
stock-market valuations. But the rise of generic copying once patents expired meant companies needing
consistently to bring new products to market or lose market-share and profits. Hence the attraction of
‘combinatorial chemistry’, the new technology of the early to mid-1990s specifically identified in
Nightingale’s influential paper. The technology involved selection of chemical molecules, and their
multiple combinations. Machines created thousands of chemicals almost overnight compared to the weeks
humans took to do combinatorial chemistry. Robots connected elements of each chemical into small vials
containing samples of a bodily substance involved in a disease -- for example, the protein triggering
production of cholesterol. If the two reacted in the desired way, a ‘hit’ was registered, latterly using
luminescence technologies in some cases. This testing process is known as high-throughput screening
(HTS). Most large pharmaceutical firms install the new machines in sites formerly housing their
laboratories, and many invested large sums on contracts with small companies specialising in HTS. For
example, GlaxoSmithKline spent more than $500 million to buy a combinatorial chemistry company.
However the automation processes failed to work as anticipated. The head of discovery chemistry at
Bristol-Myers Squibb has referred to the first five or six years of the new technology a ‘nightmare’
observing that many chemists became fixated on creating thousands or millions of chemicals for testing
without thinking about whether any of them could turn into a usable treatment. Test tube compounds were
broken down too easily in the human stomach, issues now-destroyed ‘craft-based’ capabilities formerly
embedded in traditional chemists meant were usually assessed beforehand. Some results meant scientists
were forced to wrestle intellectually with chemicals that were almost impossible to deliver in humans. The
struggle often led to serious delays in development schedules when, for example, a drug to prevent
infection might work in vitro but fail to dissolve in water, the medium used in intravenous drips (Landers,
2004). Accordingly, network interactions among large client firms and DBFs have even been reinforced in
areas like HST that were once thought by observers to herald a new age of technical treatments. Whether
these are teething troubles or not remains to be seen, but more than ten years is considered by many to be
over-long in surmounting teething difficulties1.
Can European Biotechnology Compete?
In 2002, research suggests there were 4,300 biotech companies worldwide, 630 of them public, with
$41 billion in revenues, up from $36 billion in 2001. But net losses doubled - to $12 billion.
Disparities between the US and Europe are also widening: US biotechnology firms raised $8.7
billion in equity financing in 2002, up 10% from 2001, while the finance available to European firms
dropped 40%. One of the major problems for biotechnology businesses is surviving long enough to
develop products and get them to market given clinical development takes five to eight years, then
launching two to three years, and peak sales are unlikely to appear until five years post-launch. Each
product needs an average investment of $800 million to develop, possibly up to $1.5 billion to fund
its entire lifecycle. There are three main options:
Raising finance, either privately (venture capital firms) or publicly (initial public offerings etc.)
Consolidate or cut costs
Licensing deals (a key subject of this paper)
Industry leaders believe many companies make the mistake of trying to market the wrong drug. DBFs)
would probably avoid the general practitioner market completely without a partner, whereas specialised
areas are easier to deal with. Thus many began with niche capabilities in molecules tackling ‘orphan’ or
otherwise rare diseases. Pre-marketing costs are high, especially for very innovative products, and small
companies have to consider post-launch expenses, such as pharmacovigilance costs, which can also be
Of interest here is the response to this capabilities crisis. Some pharmas began sub-contracting this function to specialist
screening firms, others have reduced scale. Bristol-Myers tried to create a better mix of high technology and old-fashioned lab
work with a screening machine capable of testing a million chemicals at once, taking as little as 10 microlitres of each chemical.
Scores of mice are available nearby allowing scientists quickly to test the ‘hits’ in animals. This avoids a problem whereby
scientists spend months or years refining a compound in a test tube only to find it doesn't work in living things. ScheringPlough's main research laboratory has a machine that allows making several dozen variations of a chemical, all in the liquid
state. The number is much smaller than older machines produce and the scientist can hand-pick the combinations and get higher
quality. Hence screening is returning towards craft-skill and computational hybrids, having demonstrated total automation to be
Recent evidence shows merger s and collaborations between biotechnology companies, geographical
clustering, and especially consolidation within the industry are all being practised, not least in Europe.
Both Celltech and Vernalis in the UK engaged in mergers and acquisitions in the downturn. Industry
monitors agree that of the 1,870 DBFs in the European Union, only 20 have sustainable profitability. 2530% of the EU's DBFs have less than one year's funding, and display a number of problems, including:
sub-critical R&D capabilities
high infrastructure costs and inefficiencies
poor business models and management
Both Celltech and Vernalis grew by acquisition to avoid such capabilities weaknesses despite management
reluctance. Moreover, valuation expectations were difficult given market conditions. But successful
merger and acquisition (M&A) strategies strengthen pipelines by keeping only the best actual or potential
products from merging companies, as well as creating cost synergies since infrastructure and facilities
represent 30-45% of the cost base of DBFs. Celltech could hypothetically acquire UK's ‘rising star’
diabetes company Alizyme (see below) though it is stated to be more interested in US acquisitions.
Ironically in 2004 it was itself acquired by Belgian chemicals company Union Chimie Belgique. Any
acquisition would have to have strategic value, and markets would probably penalise any buying of an
early-stage company - pipeline products being key. Many EU companies are now bought by US firms,
judging from Table 2, finding it easier with their higher valuations. Regarding optimal scale, it is also clear
that ‘big pharma’ mergers producing 4-5,000 researchers may not be optimum given performance. Glaxo
merging with SmithKline Beecham even split R&D into smaller units and results improved. However, it is
also clear for DBFs that even 50-70 researchers may not be enough unless DBFs are engaged in larger
networked alliances.
Regarding the option of becoming a public company on IPO flotation, there remains a funding gap
between venture capital and IPO stage. Moreover, criteria for the latter can be unreasonable (e.g. good
Phase II data, strong management). Early-stage work needed to be funded; therefore companies should
have an IPO fairly early in their existence. A UK market capitalisation of £100 million seems to be the
minimum safety position currently. Thus less than 25% of private DBFs meet the criteria for either an IPO
or major deal, and achieving critical mass remains difficult for the remaining 75%. Hence the impulse,
regarded in the industry as essential, that rationalisation and consolidation occurs. While only one IPO, for
Genitope was successful in 2003 biotechnology market indices rose 30% that year due to positive trial
results for products such as Genentech's cancer drug Avastin, and efforts by the FDA to speed the
approval process. Thus for the first time in seven years, in 2003 biotechnology overtook software as the
number one industry for VC investment, with a total of $873 million.
Larger DBF Acquirer
Versicor (US)
Protein Design Labs (US)
British Biotech (UK)
Celltech (UK)
Genaissance (US)
Chiron (US)
Genzyme (US)
British Biotech (UK)
Idec (US)
Smaller Target DBF
Biosearch Italia (IT)
British Biotech (UK)
Eos Biotech (US)
RiboTargets (UK)
Oxford GlycoSciences (UK)
DNA Sciences (UK)
Cell Pathways (US)
PowderJect (UK)
SangStat (US)
Vernalis (UK)
Biogen (US)
Resulting DBF
British Biotech
Biogen Idec
Table 2: Major Biotechnology Mergers & Acquisitions – 2003
Source: IMS Companies Information
NB: British Biotech split between OSI and Vernalis
Nevertheless small DBFs suffer from low profile and associated lack of investor interest - in the US,
largely stemming from changes to Securities and Exchange Commission regulations following the stock
market downturn, and in the EU due to a lack of interest from exchanges, indeed Germany’s Neue Markt
ceased functioning. Clearly this adds to the woes of independent small DBFs who face daily a possible
‘death spiral. This is also a cause of huge biotechnology fluctuations in share prices related to pipeline
Some venture capitalists began only in late 2003 investing in the EU again after a two year hiatus, and
biotechnology investment specialists accurately predicted the IPO window to close again from the second
half of 2004 although some Swiss and UK IPOs were again successful in 2005. Given dependence on DBF
R&D, pharmaceuticals firms have an interest in being more generous with licensing deals. Indeed by late
2004 DBFs were increasingly by-passing venture capital to make licensing deals directly with pharma.
Also given that $800 million is needed for each company to reach profitability, and only $300 million was
available for the whole sector in London, relying on venture capital alone is clearly not sustainable. For the
moment, many VCs have become more risk averse, travelling in herds, which will probably lead to a
hiatus in the innovation stream in 7-10 years' time. Even so, industry evidence represented in BIGT (2003)
showed Europe as a whole with more biotechnologically-derived drugs in late-stage trials than the US. But
usually the sustainability to market of US late-stage candidates has proved stronger, presumably due to
better development capabilities from a maturer industry.
There are official concerns in Europe about the control of domestic companies from the US yet the UK
biotechnology industry, widely seen as the strongest in Europe, is evolving a ‘conveyor belt’ or
‘decapitation’ model of offshore innovation in the US. Thus decapitation means retaining examination
knowledge capabilities in UK while accessing exploitation capabilities in the US (Ward, 2005).Thus the
UK’s ambitions regarding domestic commercialisation of high grade fundamental research have been
thwarted by capability deficiencies in commercialisation. Table 3 lists some key 2005 UK decapitations.
Cambridge, MA
Short Hills, NJ
Waltham, MA
Bloomsbury, NJ
Gaithersburg, MD
Hayward, CA
Frazer, PA
Retains R&D in Oxford, UK
Retains R&D in Dundee, UK
Reverse merger into Xcyte
Therapeutics, Seattle, WA
Retains R&D in Cambridge, UK
Retains R&D in Cambridge, UK
Acquired by Celldex Therapeutics
Retains R&D in Wokingham, UK
Acquired by Emergent BioSolutions
Retains R&D in Cambridge, UK
Merger with Lynx Therapeutics
Retains R&D in Oxford, UK
Acquired by Cephalon
Table 3: R&D Decapitations of UK Biotechnology Businesses, 2005
Source: BioCentury (Ward, 2005)
The overwhelming reasons for separation of R&D from business capabilities cited by these firms’
representatives were:
seeking US chief executives to run the business
Proximity to an experienced pool of management talent
weak state of IPO markets for biotechnology firms in UK
more sophisticated US shareholder base
Better access to higher capability (‘savvy’) biotechnology fund managers
Proximity to the deal-making hubs of big pharma
Hence, we see very clearly the effects of asymmetric knowledge capabilities in these data and
explanations, supporting the basic spatial knowledge capabilities theory of economic growth being
explored here. Europe, or more precisely the UK in this instance, has exploration knowledge capabilities
spatially concentrated on Oxford and Cambridge. These re-appear in co-publication data presented below
(Figs, 1 & 2) but the most highly developed exploitation knowledge capabilities are in US bioscience
megacentres that combine numerous of the complicit knowledge capabilities such as those listed
immediately above, ranging from ‘talent’ at CEO and manager levels, through sophisticated shareholders,
to more capable investors. This is a variant on an earlier industry view is that there will likely be
deconsolidation of some ‘big pharma’, consolidation of the medium-sized DBFs, and ‘crash and burn’ for
the rest, especially poor companies and products killed off as a more Darwinian approach to investing
leads to survival of the fittest (Howard, 2003).
To What Extent Are Knowledge Networks Globalising?
We have seen how in theoretical terms, knowledge networks are the dynamic capabilities that
Penrose (1995) theorised as metamorphosing the global economic order by transforming industry
organisation, and crucially we would argue, heralding a new theory of economic geography. As
noted, we call it the Regional Knowledge Capabilities (RKC) theory of economic geography. It is
clear how it operates in regard to Biosciences, also other S&T based industries like ICT and new
(and old) media – all heavily reliant on networks of project contracts. We see how it underpins
regional innovation systems and, within them, clusters, some of which are elsewhere termed
‘megacentres’ where they deploy the full knowledge value chain capabilities from exploration,
through examination, to exploitation knowledge (Cooke 2002a; 2005b; Cooke et al, 2004) and they
express the transition in advanced economies from the industrial to the knowledge economy2. The
innovation system is the broader geographical setting where the most important knowledge
exploration, examination and exploitation capabilities concentrate and secondary ones, attracted by
increasing returns to knowledge, including localised knowledge spillovers are found as secondary
nodes or even more diffused networks. There is an established theoretical basis for these processes in
the work of Myrdal (1957) and Hirschman (1958) from whom Krugman learned sufficient to
advocate a theory of spatial monopoly based on ‘increasing returns to scale’. However we do not
find the self-confessedly ‘simplistic’ two-location, zero-sum model of spatial monopoly as advocated
by Krugman (1995) convincing.
This is mainly because it is clearly wrong in respect to the evolution of knowledge clusters, the
largest of which may, as noted, act as megacentres defined by their geographic concentration of the
full knowledge value chain of a ‘related variety’ technology ‘platform’. These compatibilities exist
in symbiosis with lesser and differentiated nodes and networks. Crucially, unlike the Industrial Age
when these were spatially separated into spatial divisions of labour (Massey, 1984) knowledgedriven innovation systems concentrate practically everything in a region or regions with a sectoral
The rise of brain over brawn as a key capability especially since the 1980s is a widely observed feature of contemporary
advanced and even developing economies where capital mobility, offshoring and outsourcing (including R&D) to the likes of
China and India are merely the latest manifestation of the quest for knowledge capabilities from global talent pools. On this
latest mutation in globalisation see Cooke (2005a). For a more polemical view of global ‘flattening’ in pursuit of knowledge
capabilities, see Friedmann (2005)
knowledge epicentre in an urban or metropolitan R1 (first class research) university-laboratory
complex linked to more and less proximate complementary nodes and networks.
Figs. 2 & 3 give original data on global bioscientific knowledge networks involving elite institute,
‘star’ scientist research co-publication for the period 1998-20043. Fig. 2 concentrates on copublication in leading US journals, Fig. 2 focuses on the same in leading European journals. Here we
see dense international publishing networks. What does Fig.2 reveal? The following four aspects are
of obvious theoretical and empirical interest. An international collaborative biosciences publication
core of ‘star’ scientists and leading research institutes clearly exists. In the US it is centred upon
Boston, Cambridge, MA, San Francisco, San Diego and New York City – the last-named being
strong in research but less so in commercialisation. Second, there is a penumbra of various lesser
research nodes centred upon Stockholm, Cambridge & Oxford (UK), Singapore, Paris, Toronto and
Tokyo. These often have a few or one strong network partner in one of the US meagacentres. The
two Cambridges are relationally proximate, as are Pasteur Institute in Paris and New York University
or Karolinska Institute in Stockholm with Harvard Medical School. Beyond that for publication in
top US journals is a ‘third circle’ of the lesser co-publishing locales including the likes of Hebrew
University, Jerusalem, Uppsala University, University of Montreal, Oxford and London universities,
and the National University of Singapore. Third, notice that among the ‘penumbras’ there are also
co-publication links but far weaker than those through the network hierarchy to the US megacentres.
Finally, notice by contrast the strong intra-nodality of linkages among co-publishers in geographical
proximity, optimising localised ‘global capabilities’ especially in the aforementioned US
megacentres but also elsewhere to a lesser extent, as in London, Cambridge, Oxford and Toronto.
The methodology used here is innovative and one of the first to trace regional bioscience node linkages to track globalisation
of bioscientific research publication networks. Three steps precede search for linkages. First, identify all relevant publishing
institutions (including DBFs) in the hypothesised node (e.g. Cambridge UK Biotechnology Institute; Cambridge MA,
Whitehead Institute; San Diego, Scripps Institute, etc) then identify leading institutes by presence of leading publishers from
websites. Third, crosscheck and measure these by publication in top-ranked international (English-language ) bioscientific (e.g.
Nature Biotechnology) journals (using SCI citation rankings). Journals consulted are shown in Appendix 2.
San Diego
San Fran
New York
Cambridge (MA)
Fig. 2: Bioscientific Co-publication in Leading US Bioscience Journals
More qualitative research by Owen-Smith & Powell (2004) on Boston alone highlighted as follows key
processes by which dynamic place-based capabilities are expressed in research or exploration knowledge
transfer, and beyond tat in commercialisation or exploitation knowledge transfer.
The difference between ‘channels’ (open) and ‘pipelines’ (closed). The former offer more
opportunity for knowledge capability enhancement since they are more ‘leaky’ and ‘irrigate’ more,
albeit proximate, incumbents. Pipelines offer more capable means of proprietary knowledge
transfer over great geographical distances based on contractual agreements, which are less ‘leaky’
because they are closed rather than open.
Public Research Organisations are a primary magnet for profit-seeking DBFs and large
pharmaceuticals firms because they operate an ‘open science’ policy, which in the Knowledge
Economy era promises innovation opportunities. These are widely considered to be the source of
productivity improvement, greater firm competitiveness, and accordingly economic growth.
Over time the PRO ‘conventions’ of ‘open science’ influence DBFs in their network interactions
with other DBFs. Although PROs may not remain the main intermediaries among DBFs as the
latter grow in number and engage in commercialisation of exploration knowledge and exploitation
of such knowledge through patenting, they experience greater gains through the combination of
proximity and conventions, than through either proximity alone or conventions alone. This is
dynamic knowledge networking capability transformed into a regional capability, which in turn
attracts large pharma firms seeking membership of the ‘community’.
These propositions each receive strong support from statistical analyses of research and patenting practices
in the Boston regional biotechnology cluster. Thus:
‘Transparent modes of information transfer will trump more opaque or sealed mechanisms when a
significant proportion of participants exhibit limited concern with policing the accessibility of
network pipelines…closed conduits offer reliable and excludable information transfer at the cost of
fixity, and thus are more appropriate to a stable environment. In contrast, permeable channels rich
in spillovers are responsive and may be more suitable for variable environments. In a stable world,
or one where change is largely incremental, such channels represent excess capacity’ (Owen-Smith
& Powell, 2004)
Finally, though, leaky channels rather than closed pipelines represent also an opportunity for unscrupulous
convention-breakers to sow misinformation among competitors. However, the strength of the ‘open
science’ convention means that so long as PROs remain a presence, as in science-driven contexts they
must, such ‘negative social capital’ practices are punishable by exclusion from PRO interaction,
reputational degrading or even, at the extreme, convention shift, in rare occurrences, towards more
confidentiality agreements and spillover-limiting ‘pipeline’ legal contracts.
Now let us turn to Fig. 3 which displays the global network configuration of bioscientific copublications among researchers targeting top European journals. First, it is obvious that the global
San Diego
San Fran
New York
Cam (MA)
Fig. 3: Bioscientific Co-publication in Leading European Bioscience Journals 1998-2004
network hierarchy is somewhat, though not entirely inverted. The US is still rather strong, although New
York City far less so than the other four US megacentres, while Oxford and Cambridge (UK), London and
Stockholm-Uppsala become more prominent. Second, a significant proportion of US ‘star’ scientists copublish with a European journal target in mind, but it should be noticed that co-publication intensities are
much lower for European than US journals, signifying the relative global prestige of the latter. Finally
quite solid co-publication in European journals entails co-publication of research findings among many of
the ‘penumbra’ clusters as well as with US-based researchers. Some modestly distant relational proximities
among the latter include Pasteur Institute and Copenhagen University, Zurich and Geneva universities with
Uppsala University, and Copenhagen-Lund-Stockholm. But more distant networks are found between
Montreal and Singapore, Toronto and London and Tokyo and Jerusalem. But again, hierarchical links to
stronger European as well as US nodes remain pronounced. The global biosciences co-publishing network
is unquestionably not a flat but a hierarchical one advantaging the US though to a lesser extent..
Emergent Biotechnology Markets
In this section we shall briefly examine developments in five developing network nodes, most of which
were mentioned in the previous section of this paper as having research or market relationships with UK
firms (and conceivably US firms) which it can be hypothesised were less pronounced hitherto. The
countries in question are Canada, Sweden, Switzerland, Singapore and Israel. For the present, space does
not allow investigation of scientific and market developments in two key biologics supplier countries of
the future, India and China. How have these five nodal countries positioned themselves globally? What
specialisation, if any characterises their activity? To what extent are institutional and economic geography
patterns established in the US and UK being repeated? For example in EU countries considered elsewhere,
clustering near major knowledge centres like Munich in Germany and Paris, France characterise the
institutionalised economic geography of the sector (Cooke, 2002b; Kaiser, 2003; Lemarié, Mangematin &
Torre, 2001). Literature on which the following brief accounts are drawn ostensibly shows the same, with
Canada’s biotechnology dominated by Montreal and Toronto’s clusters, Sweden having concentrations in
Stockholm-Uppsala and Lund-Malmö, now bridged formally to Denmark’s nearby Medicon Valley cluster
in Copenhagen, Switzerland’s concentrations are at Zurich, Geneva and Basel, Singapore’s tightly drawn
to the National University of Singapore campus, and Israel’s main concentration is in Jerusalem, near to
the Hebrew University and Hadasit incubator (Niosi & Bas, 2003; McKelvey, et al., 2003; VINNOVA,
2003; Nelund & Norus, 2003; B3 2002; 2003; Finegold et al., 2004; Kaufmann et al., 2003). However, as
will be shown the trajectories by which these concentrations reached fruition are distinctive. One group
has, in individually distinctive ways, an origin in close relationships with ‘big pharma’. In Sweden and
Denmark corporate spinout and supply, the former following Pharmacia’s acquisition by Pfizer, combine
with university research and associated start-up DBFs. In Switzerland, close links to Roche and Novartis,
the latter with its own incubator and VC fund, influence the focus in Basel. But Zurich and Geneva also
have biotechnology and university research is a key progenitor. While Singapore’s growth is based on a
determined FDI strategy to attract global leaders but also interact through university research with DBFs.
In Canada and Israel the origins of their metropolitan clusters lie primarily in public research funding and
academic entrepreneurship. Let us now explore in a little more depth the experiences, linkage specificities
and global knowledge network patterns and processes in the two broad categories.
Economic Geography of Clusters Spawned by Pharmaceuticals Firms
In the three instances under the spotlight here – Sweden/Denmark, Switzerland and Singapore the
influence of ‘big pharma’ is, as noted, pronounced. Thus the first commercial exploitation of modern
biotechnology in Sweden was based on technology from Genentech, licensed by the Swedish company
Kabi in 1978. Kabi merged with Pharmacia in 1990. Pharmacia later merged with two US companies,
Upjohn and Monsanto, to form Pharmacia Corporation. In the spring of 2003 Pfizer, the US
pharmaceutical company acquired Pharmacia Corporation. The other major pharmaceutical company in
Sweden, Astra based in Gothenburg and Lund (now a Swedish-UK firm Astra-Zeneca) started using
recombinant DNA technology in the late 1980s. From then, and increasingly in the 1990s, new DBFs were
founded in Sweden. Most of these new companies were either spin-offs from university research or from
existing large pharmaceutical companies. Swedish biotechnology places fourth in Europe in terms of
number of companies and number nine in the world according to the Swedish Trade Council in 2002.
The number of Swedish DBFs, increased by 35 percent from 135 in 1997 to 183 in 2001, and the number
of employees increased by 48 percent to about 4,000 (VINNOVA, 2003). The two pharmaceutical
companies AstraZeneca and Pharmacia Corporation were the dominant large companies engaged in
biotechnology activities. Many Swedish DBFs serviced them in the biopharmaceuticals application sector,
but also in such industries as food processing and agriculture. These DBFs are highly research-led and
knowledge-intensive. Between 10 and 20 percent of employees in these companies have a doctoral degree.
Of company presidents responding to VINNOVA’s questionnaire, 93 percent stated that their companies
collaborated with academic research groups. From our earlier results, this appears to differentiate Swedish
DBFs somewhat from those in the US and UK where research interactions are more among firms or
distinctively among PROs. This is possibly an indicator of the relative immaturity of many Swedish DBFs,
formed as we have seen in the 1990s for, according to the VINNOVA study, a majority of companies were
small in 2001, that is, had fewer than 200 employees. Almost 90 percent of the companies had less than 50
employees, and a good half had less than 10 employees. However, the category of small and medium-sized
biotech companies is growing such that in 2001 Swedish DBFs totalled about 4,000 employees, a 35
percent increase since 1997.
These are mostly found clustered in Sweden's metropolitan regions and in cities with large universities
conducting substantial medical research. Fifty-six DBFs are located in the Stockholm region, followed by
the Lund/Malmö and Uppsala regions, with 36 and 31 respectively. 24 are located in the Gothenburg
region. The smallest cluster is in the Umeå region, with fewer than ten biotech DBFs. The Swedish
pharmaceutical industry annually spends around 25 percent of its revenues on R&D, higher than the global
standard of 17.5%. This high percentage by international standards mainly reflects AstraZeneca's large
expenditures in its Swedish research centres, with around one third of the group's total R&D investments,
$3.1 billion, occurring in Sweden. Of this some 20% or $540 million is spent extramurally in
Sweden (Benner & Sandström, 2000). Stockholm-Uppsala, in particular contributes to Sweden’s relative
strength in biotechnology research, mainly through the Karolinska Institute, Uppsala University and the
Royal Institute of Technology. These produce annually some 8,000 publications, co-host some 4,000 PhD
students and employ some 2,200 scientists. Together with Switzerland and Denmark, Sweden publishes
the largest number of scientific articles in the world in relation to population, as Table 4 makes clear
(Jacobsson, 2002)
Table 4: Top Ten Biotechnology Publication Rates Per Capita 1996-8
Source: based on Jacobsson (2002)
However, on co-publication interactions, Sweden’s collaborations are rather inward-looking, in that 70%
of co-authorships 1986-1997 were with other Swedish PROs, while 12% were with US institutions and an
equivalent share with UK and German co-authors together. However, regarding R&D projects McKelvey,
Alm & Riccaboni (2002) found the opposite, that is of 215 collaborations made by 67 actors (firms,
universities and research institutes), 52 were between Swedish institutions and the rest involved overseas
partners, these again being mainly with the US and UK.
Singapore’s government biotechnology initiatives started in 1987 with the establishment of the Institute of
Molecular & Cellular Biology at the National University of Singapore, but became industrially serious
within the 200-2004 period. The aim was to build a biotechnology cluster around foreign direct investment
(FDI), a policy that worked well in previous developmental stages such as the policies in support of
petrochemicals, electronics and ICT. Four new Institutes in bioinformatics, genomics, bioprocessing and
nanobiotechnology now exist at a cost of $150 million to 2006. Public venture capital of $200 million has
been committed to three bioscience investment funds to fund start-ups and attract FDI. A further $100
million is earmarked for attracting up to five globally leading corporate research centres. The Biopolis is
Singapore's intended world-class R&D hub for the georegion. The Biopolis is dedicated to biomedical
R&D activities and designed to foster a collaborative culture among the institutions present and with the
nearby National University of Singapore, the National University Hospital and Singapore’s Science Parks.
Internationally celebrated scientists have also been attracted, such as Nobel laureate Sidney Brenner, Alan
Colman, leading transgenic animal cloning scientist from Scotland’s Roslin Institute, David Lane,
discoverer of the p53 ‘guardian angel’ anti-cancer gene also from Scotland, Edison Liu former head of the
US National Cancer Institute, and leading Japanese cancer researcher Yoshaki Ito.
These are ‘magnet’ appointments meant to attract talent and create cluster conventions and practices
among research centres and DBFs. The sector now numbers 38 firms of which 15 are indigenous start-ups
and 23 FDI R&D, manufacturing, clinical research organisations (CRO) and other services. Johns
Hopkins, MIT, Duke University, Columbia University and the Indian Institute of Technology have
established facilities in Singapore. Singapore’s Bioethics Advisory Committee advised acceptance of
embryonic stem cell but not human cloning research, which is also a globally attractive locational factor,
shared as we shall see with Israel, among others. Pharmaceuticals firms from overseas manufacturing in
Singapore include Glaxo since 1989, Schering-Plough (1997), Genset (now Serono) (1997), Aventis (now
Sanofi) (2000), Merck (2001), Wyeth (2002), and Pfizer (2004). R&D Centres of the following firms are
also present: Genelabs (1985), Becton Dickenson (1986), Oculex (1995), Perkin Elmer (1998) Sangui
(1988),Cell Transplants (2000), Schering-Plough (2000), Eli Lilly (2001), Surromed (2001), Affymetrix
(2001), Novartis (2002), ViaCell (2002), PharmaLogicals (2002) Finally, CRO firms include: Quintiles
(1995), Novo Nordisk (1999), Covance (2000) and Pharmacia-Upjohn (now Pfizer) (2000). Joining
ViaCell in stem cells are indigenous DBFs ES Cell and CordLife, a few genomics firms like APGenomics
and Qugen, and a variety of drug discovery, bioinformatics and diagnostics firms mostly established since
2000. In brief, Singapore is host to a large number of mainly US and, to lesser extent European R&D
laboratories of ‘big pharma’ businesses. It has strength in public research activity and small signs of
growth in stem cells exploration and exploitation activity. The benign regulatory environment allowing
embryonic stem cells research is undoubtedly an attraction which, contextuated by Singapore’s celebrated
‘developmentalist state’ capabilities, will stimulate cluster growth as an ‘offshore’ research and production
platform targeting the burgeoning Asian market.
Switzerland is another small country in which multinationals generally, and in ‘big pharma’ specifically,
are a notable feature of the economic landscape. Roche (Hoffmann La Roche) and Novartis (formerly
Sandoz and Ciba-Geigy) are indigenous Swiss multinationals, the latter in the global top ten by market
capitalisation, the former having slipped down the rankings in latter years rather like Bayer and, to some
extent Aventis among former European majors. The Swiss government too in 2002 announced in favour of
embryonic stem cells research while banning embryo-creation purely for research purposes. Apart from
Novartis and Roche Switzerland also hosts one of the world’s largest biotechnology companies Serono,
which in 2001 had a market capitalisation of $18 billion, ranking it third behind Amgen and Genentech.
Serono and Amgen signed a licensing and commercialisation deal for Serono to sell a Multiple Sclerosis
drug in the US it had developed with Immunex, a Cambridge (MA) firm subsequently acquired by Amgen.
Other prominent firms are Actelion, Cytos, The Genetics Company, bio-T, CELLnTEC, Debiopharm,
GeneBio and Solvias. Lonza Biologics is also Swiss and one of the largest biosynthesis firms in the world.
Debiopharm funds cancer research projects at Tulane University, New Orleans.
Of the 200 Swiss biotechnology companies listed in the Swiss Life Sciences Database
( in 2003 around 40 are pure biotechnology firms (DBFs), the others being
instrumentation and services firms that nevertheless link to many of the forty. Some 22% of the 200 are
located in the Geneva-Lausanne ‘BioAlps’ region, approximately 26% are in the Basel ‘BioValley’ region,
and about 35% are in the Greater Zurich region. Zurich has a Functional Genomics Research Centre. Since
2000, 45 new biotechnology businesses were established, 15 of which were spinouts from the Swiss
Federal Institute of Technology in Lausanne and Zurich and 10 were spinouts from other Swiss
universities. The remainder came from domestic and foreign subsidiary industry. Of the thirty or so public
companies, many like Genedata (Basel), Cytos (Zurich) and GeneProt (Geneva), a proteomics DBF, have
long-term collaborative research, opinion and licensing agreements with the likes of Novartis and Roche.
International collaborations extend to the partnership between the University of Minnesota and the Swiss
Federal Institute of Technology focused upon medical technology. This arises in part from Minneapolis
devices firm Medtronic’s subsidiary located in Switzerland and its collaboration with the likes of
Disetronic, a leading Swiss insulin-pump manufacturer. In conclusion, Switzerland is a small, capable
knowledge-intensive biosciences economy. It has leaders in ‘big pharma’, global DBF capability and
numerous smaller DBFs and spinouts concentrating on leading edge proteomics and other post-genomics
treatments. It is highly connected globally, but especially to the US megacentres through ‘big pharma’ and
its leading clusters in Zurich, Basel and Geneva-Lausanne.
The Research and DBF-led Clusters in Israel and Canada
By 2001 the Israeli biotechnology industry consisted of 172 firms, seven publicly traded and the whole
with a total valuation of $3.5 billion. 4,000 employees worked in the sector and 85% of firms employed
less than twenty persons. These firms a re clustered as follows: Rehovot (satellite of Tel Aviv) has 46
firms and some 1,290 life scientists, Jerusalem has 38 firms and 1,358 life scientists, and Tel Aviv has 32
firms and some 1,725 life scientists. Jeruslaem’s patent score is the highest at 209 followed by Rehovot at
176 and Tel Aviv on 64. Jerusalem has perhaps the more mature of thee three clusters, something the
patent data underline. Apart from generics drug manufacturer Teva, Israel lacks major pharmaceuticals
firms and even CRO services. It is very much a research-led, university and incubator-based system in
much need of finding co-incubation partners and consolidating links with ‘big pharma’. It is noteworthy
that if Hebrew University, Jerusalem were to appear in table 1 on university royalty earnings it would be
third because of its 2002 licensing income of over $60 million resulting from the invention in its
biosciences faculty of the globally-consumed ‘cherry tomato’ Kaufmann et al., (2003).
The Canadian industry is split evenly between Toronto and Montreal. Space only permits limited
discussion of both, starting with Toronto. According to Niosi & Bas (2001; 2003) Toronto has the
stronger scientific base than Montreal even though each city has some 73 and 72 DBFs respectively. Thus
Toronto had 1989-99 178 DBF patents, 61% of the total while Montreal had 51 or 17%. Similarly Toronto
had 191 patents overall including firms, universities and government (37%) and Montreal 96 (18%).
Toronto has been recently named as the destination of Aventis’ new $350 million Genomics Research
Institute, adding to ‘big pharma’s’ exodus of leading edge research from continental Europe (Novartis has
moved its equivalent $250 million facility to the Boston region). Venture capital also reflects the variation
in status of the two centres. Both cities have multinational pharmaceuticals firms like Pfizer, Merck, Shire
(formerly BioChem Pharma), Aventis, Novartis and Schering in Montreal and Aventis Pasteur,
AstraZeneca, Bayer, Eli Lilly in Toronto. Each has main university research centres such as University of
Toronto, York, and McMaster in Toronto and McGill, University of Montreal, University of Quebec at
Montreal and Concordia universities in Montreal. Many government research laboratories also co-exist in
both places
Yet there is more of a struggle for the DBF sector in Montreal since the region experienced significant
industrial restructuring, loss of traditional manufacturing industry, and particularly financial services to
Toronto during the 1970-2000 period. However, bioscientific research is deeply embedded in the region
and commercialisation has produced world-class firms, notably and also at the Laval campus, BioChem
Pharma discoverer of Epivir, the first AIDS treatment marketed through Glaxo. Since acquired by UK
firm Shire Pharmaceuticals, BioChem was in late 2003, experiencing significant corporate re-positioning
towards less costly therapeutic targets while advanced research was being focused more towards home
base. the greater Montreal region. The region has a large biomedical cluster with leading companies and a
strong research base with four universities in Montreal, an established biopharmaceuticals industry with
145 companies and 14,500 jobs and 50 biological research institutes including the Canadian National
Research Council Biotechnology Research Institute, an important federal biotechnology research centre.
QBIC is located in the Laval Science and High Technology Park, Montreal. The Park was created in 1989
as the result of a strategic alliance between the INRS-Institut Armand-Frappier, (a research centre of
Quebec University) the City of Laval and Laval Technopole. The Laval Science and High Technology
Park is the focus of “Biotech City”, a $100 million initiative launched in June 2001 to develop a business
and science centre supported by the Quebec government, Investissement Québec, the Institut National de
la Recherche Scientifique (INRS), the Laval Technopole and the City of Laval. Some 30 businesses,
biotechnology and biopharmaceutical companies, research centres and IT firms exist in Biotech City.
QBIC had, in late 2003, six firms in its bioincubator (Cooke et al., 2006).
We can say, in conclusion that Canada’s large pharmaceuticals firms have grown as established
production, sales and marketing branches serving the Canadian market whether from the US or Europe.
However cluster-development has occurred mainly separately from this through academic
entrepreneurship supported by well-found research infrastructure and local venture capital capabilities.
Unlike Israel, where there are scarcely any pharmaceuticals firms with which DBFs might interact, in
Toronto and Montreal, they have been perhaps equivalents of ‘ships that pass in the night’. In relation to
our earlier proposal of a Spatial Knowledge Capabilities theory of economic geography, the sketches
provided above point to the emergence in some cases in concertation of DBFs and ‘big pharma’, in others
San Francisco 152
San Diego
Stockholm-Upp. 87
Lund-Medicon 104
Life Scientists
Big Pharma Funding
$601.5 m.
$1,063.5 m.
$432.8 m.
$120.0 m.
$60.0 m.
$400.0 m.
$90.0 m.
$ 80.0 m.
$250.0 m.
$57.0 m.
$200.0 m.
$300.0 m.
$800m./annum 96-01
$400m./annum 96-01
$320m./annum 96-01
$54 million (2001)
$250 million (2002)
$300 million (2002)
$105 million (2000)
$88 million (2001)
Table 5: Core Biotechnology Firms, 2000: Comparative US and European Performance Indicators
Source: Cortright & Mayer, 2001; NIH; NRC; BioM, Munich; VINNOVA, Sweden; Dorey, 2003; ERBI, UK,
Kaufmann et al, 2003.
the largely or substantially independent emergence of DBF clusters in proximity to knowledge centres,
notably universities where leading edge research is commonly practised. To these centres are attracted
investment and talent, including in recent years incumbent research talent in ‘big pharma’ that also moves
its ‘ahead of the curve’ post-genomics research to look-out posts or embedded research facilities in the
clusters. No longer does talent migrate to corporate headquarters in New Jersey or London, rather the
magnets for large scale knowledge investments are the privileged global network nodes represented in a
brief benchmarking exercise in Table 5, which includes also Munich, hence a brief orientation sketch is
desirable for completeness. In Munich, Martinsreid in the south western suburbs marks the centre of
biotechnology research and incubation in Bavaria. Aventis opened its Centre for Applied Genomic
Research there and the Biotechnology Innovation Centre (IZB) funded €80 million by the Bavarian
government is located nearby with 9,000 square metres of laboratory and office space. The organization
responsible for managing development of biotechnology, BioM, is also located at Martinsried. The area
has become a biomedical research campus with 8,000 researchers working in biology, medicine,
chemistry and pharmacy located there. BioM AG is a one-stop shop with seed financing, administration
of BioRegio awards and enterprise support under one roof. Seed financing is a partnership fund from the
Bavarian State government, industry and banks up to €600K per company. BioM’s investments are
tripled by finance from tbg (a public investment fund) and Bayern Kapital, a special Bavarian financing
initiative. The latter supplies equity capital as co-investments. The fund has €200 million for supporting
biotechnology activities. Bay BG, and BV Bank-Corange-ING Barings Bank have special public/private
co-funding pools, and a further eight (from sixteen) Munich venture capitalists in the private-market
sector invest in biotechnology. By 2003, numerous start-ups had been funded to the tune of €120 million
with a third of this coming from BioRegio sources. DBFs increased from 36 to 120 between 1996 and
2001 (Kaiser, 2003). BioM is a network organization, reliant on science, finance and industry expertise
for its support committees. It also runs young entrepreneur initiatives, including development of business
ideas into business plans and financial engineering plans. Business plan competitions are also run in
This paper has explored in some detail the new economic geography of science-driven and knowledgebased industry. It was postulated at the outset that this had taken on a spatial life of its own in biosciences,
whereby universities and other research institutions had become magnets for the attraction of advanced
talent and knowledge infrastructures, including those of large multinational pharmaceuticals companies
eager to learn and collaborate in the exploitation of new bioscientific knowledge. Unlike the
Schumpeterian ‘creative destruction’ perspective this did not appear to produce significant neutralisation
of competitors, but rather increasingly futile merger and acquisition of ‘big pharma’ by larger ‘big
pharma’ firms. These seem to do little for improving the innovativeness of such firms whose ‘pipelines’
are drying up. The new capabilities in demand are those that reside in ‘knowledge networks’ as Penrose
(1995) refers to the dynamic capabilities to manage externalised knowledge, and these are led by small
and medium-sized dedicated biotechnology firms in clusters around the aforementioned universities. To
tie many disparate established and new concepts addressing evolutionary economic geography of the kind
under inspection here, a theoretical framework was formulated to capture key process characteristics (Fig.
Such clusters, their regional settings and distant network connections have special institutional
characteristics derived from and reinforcing geographical propinquities. The key institutions are those of
‘open science’ that renders communication networks ‘leaky’ channels rather than closed pipeline conduits
of knowledge. The latter ‘pipelines’ are more typical of a secretive ‘Industrial Age’ than a knowledge
economy. Yet there is a fascinating tension between knowledge ‘leakiness’ and the theorised ‘club’-like
character of clusters, the resolution to which must involve socio-economic mechanisms of inclusion and
exclusion. It was further shown how universities are facing the knowledge economy by increasing their
licensing income but continuing to practice ‘open science’ and increasingly doing so internationally
between growing global network nodes of bioscientific excellence. Thus increasing returns do not only
bring regional (quasi-)monopoly but also Myrdalian (1957) ‘spread effects’ into Hirschman’s (1958)
satellites of innovative economic activity. Star bioscientists reveal preferences for co-publishing in
continental propinquity, although not exclusively concerning the highest impact research institutes, and
with others in recognisable cluster-type proximity. This suggests capabilities are shared globally to a
greater or lesser extent but that Europe has few lessons to learn regarding ‘open systems architecture’
(Best 2001) from a US that remains relatively regionally introverted (at least in terms of collaborative
Finally brief accounts were given that compute well with the theoretical framework by showing clustering
to be burgeoning in the bioscientific sector in hitherto considered unlikely knowledge-capable regions like
Singapore and Jerusalem, and to be making strong contributions the advanced exploration and
exploitation knowledge development in regions like Zurich and Geneva in Switzerland, and StockholmUppsala with Lund-Medicon Valley in Scandinavia. These are now also making close network linkages
with each other in such fields as stem cell technology where bioethical determinations have given not
competitive but constructed advantage to the future development of their capabilities. Summarising, the
US still dominates a global knowledge and commercialisation network hierarchy and with scientific
investments of significant scale being made in biosciences worldwide, this means continued availability of
offshore niches that will cause ever-greater integration in the sector, but the driver for this integration is
no longer the spatial divisions of labour within the organisation of industry but the regional knowledge
capabilities of bioscientific knowledge ‘megacentres’ focused upon leading international (R1) universities
and associated research institutions, centres and laboratories.
This paper was first read to a highly crowded but lively audience at the Association of American
Geographers’ Centennial Conference in Philadelphia, March 16, 2004. Thanks to those, including
Sharmistha Bagchi-Sen, Helen Lawton Smith and Björn Asheim who organised the regional innovation
session, and to all those who responded constructively to the presentation. I’m grateful to Ed Malecki for
e-mailing me links to key Wall Street Journal articles on biotechnology. I also owe a debt of gratitude to
Ann Yaolu who assisted me with the scientometrics. Furthermore I would like to thank the anonymous
reviewers and especially editors Angel and Asheim for pushing me (to the edge of reason) to clarify the
theoretical framework. Thanks also for productive discussion of the ‘club’ versus ‘monopoly’ aspects of
clusters that took place during and after my Faculty of Geographical Sciences seminar at the University of
Utrecht, Netherlands in February, 2006 in celebration of Ron Boschma’s inaugural chair lecture, also
involving his colleague Koen Frenken. The usual disclaimer applies.
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Appendix 1: Glossary
AGY: AGY Therapeutic Inc (San Francisco)
UMA: Macquarie University (Sydney)
BaI: The Babraham Institute (Cambridge
UL: University of London
BCR: Blood Ctr Pacific (San Francisco)
UO: Oxford University
BI: The Burnham Institute (San Diego)
UR: Rockefeller University (New York)
BPRC: Biomedical Proteomics Research Centre (Geneva)
UT: Toronto University
BRI: Biotechnology Research Institute (Montreal)
UU: Uppsala University
BSI: Biosignal Inc. (Montreal)
UZ: Universtiy of Zurich
CI: Cytokinetics, Inc (San Francisco)
VAMC: Vet Adm Med Ctr, San Diego
CAT: Cambridge Antibody Technology
UHo: University Hospital (Uppsala)
CU: University of Cambridge
UM: McGill University
DL: Danish Lithosphere Centre
ULIC: Univ London Imperial College
ULu: University of Lund
UNSW: University of New South Wales
DSI: Data Searching Institute (Singapore)
EBI: European Bioinformatics Institute (Cambridge)
ETH: ETH Zurich
GEG: Gene Expression Group (Cambridge)
GRMI: Groupe de Recherche sur les Maladies Infectieuses du Porc (Montreal)
GUFB: Geneva University Faculty of Medicine
HU: Harvard University
HU: Hebrew University (Jerusalem)
IMRE: Institute of Mat Res & Engn (Singapore)
IDUN: IDUN Pharmaceuticals, Inc (San Diego)
JRH: John Radcliffe Hosp (Oxford)
KH: Karolinska Hospital (Stockholm)
KI: Karolinska Institute (Stockholm)
LL: Loma Linda (San Diego)
MI: Microbia, Inc (Cam, MA)
MIT: Mass Inst Tech
MSH: Mt Sinai Hospital (Toronto)
MSSM: Mount Sinai School of Medicine (NY)
NUS: National University of Singapore
NVI: National Veterinary Institute (Uppsala)
OHC: Churchill Hosp, Oxford Haemophilia Ctr
PC: Pharmacia Corporation (Stockholm)
PDNRC: Parke Davis Neuroscience Research Centre (Cambridge)
PI: Pasteur Institute (Paris)
RFU: Royal Free & Univ Coll (London)
RIT: Royal Institute Technology (Stockholm)
RL: Rudbeck
SC: Supercomp Ctr (San Diego)
SGI: Structural Genomix Inc, (San Diego)
SI: The Salk Institute for Bioscience Studies (San Diego)
SqI: Sequenom Inc, (San Diego)
SIB: Swiss Institute of Bioinformatics
SLRI: Samuel Lunenfeld Research Institute (Toronto)
SRI: The Scripps Research Institute (San Diego)
SU: Stockholm University
SUAS: Swedish University of Agricultural Sciences (Uppsala)
TML: Toronto Med Lab
TI: Tularik Inc (San Francisco)
UC: Cornell University
UC: Copenhagen University
UCL: University College of London
UCSD: Universtiy California, San Diego
UCSF: University California, San Francisco
Appendix 2: Sources of Data for Graphics 3 & 4
I: European Journals
1. Nature ( 1998-2004): 305 articles were checked,*
2. Nature Biotechnology (2000-2004): 520 articles in this journey were checked
3. Nature Genetics (1998-2004): 810 articles were checked
4. EMBOJ (European Molecular Biology Organization Journal) (2000-2004): 2050 articles were checked
II: American Journals
5. Cell (2002-2004): 1275 articles were checked
6. Science (1998-2004): 1030 articles were checked*
7. Proceedings of the National Academic of Sciences (2002-2004): 950 articles were checked*
8. Genes and Development (2000-2004): 346 articles were checked
The total number of articles checked: 7286
For these multi-subjects journals, only articles limited to the field of bioscience were selected.
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