The biotechnology industry: Systems of innovation and lessons learnt
December 2011
Christina G. Siontorou and Dimitris K. Sidiras
Laboratory of Simulation of Industrial Processes
Department of Industrial Management & Technology
80, Karaoli & Dimitriou Str., 18534 Piraeus
GREECE
csiontor@unipi.gr; sidiras@unipi.gr
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The biotechnology industry: Systems of innovation and lessons learnt
Abstract
To succeed in the market, companies have to address the marketing issues and tackle
them adequately. Biotechnology companies are a special case, because the strategic
decisions and the marketing issues to handle may not be ordinary. In fact, due to the
science based nature of the sector, most companies are technology intensive, being
often involved in the development of highly innovative products within a new, still
evolving, field, while struggling to gain a leading edge over competitors in the
market. Thus, companies have to leverage their technological capabilities by selecting
R&D projects that lead to a competitive advantage, still leaving ample space for
knowledge creation that will feed a system of innovation serving both, market success
and academic excellence. The inevitable university-industry interplay is not always
straightforward, owing to the different scopes and strategies of each side. This works
presents the challenges to knowledge exploitation, using the case of biosensors as an
exemplar for highlighting efficient knowledge transfer mechanisms from lessons
learnt.
Keywords:
system of innovation, product development, technology frames,
university-industry alliance
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1. Innovative product development
Biotechnology is having an increasingly important impact on the
environmental, agricultural, pharmaceutical, energy and industrial sectors, providing
innovations in genetic engineering, diagnostic devices and tissue/culture engineering
(Fumento, 2003). By 2010, the worldwide industry was represented by over 6,200
public and private biotechnology companies (Ernst & Young, 2011), 64% of which
small to medium enterprises (SMEs) hatched from academic spin-offs, concentrated
in four key markets: US, Canada, Europe, and Asia/Pacific. The US is leading the
biotechnology industry with over 60% of global biotechnology revenues; Canada
holds the second place, numbering over 400 companies. Europe’s biotechnology
market continues to grow with more product approvals and venture capital financing
than ever. The Asia/Pacific market is emerging and expanding across the region with
Australia, China, India, and Singapore dominating this market (Ernst & Young,
2009). As the biotechnology industry has become more geographically diverse,
companies have pursued more cross-border collaborations; global competition in the
sector has also increased as investors search for the best deals and most promising
technologies, regardless of location (Thompson and Vonortas, 2005). However, new
product launching is still limited due to the relatively low rate of successful final
products, especially regarding pharmaceuticals and biomedical devices (Thompson
and Vonortas, 2005), owing to the strict regulatory framework and increased expenses
for clinical trials, a burden which cannot be solely undertaken by SMEs. For example,
while 24 biotech drugs, vaccines, and new indications and uses were approved in the
US in 2001, there were over 450 still in the clinical trial phase (Thompson and
Vonortas, 2005). Even when a product finally reaches the market, its success is not at
all guaranteed. Monsanto, for example, being the early leader in agro-biotechnology,
invested heavily on insect-resistant genetically modified crops; although the products
were a major technological success, Mosanto’s efforts to introduce them to the market
failed due to negative public opinion (Chataway et al., 2004). The company, whose
share prices had fallen dramatically, was sold to Pharmacia, a European
pharmaceutical company that shortly announced its intention to sell the agro-chemical
division but was unable to find a buyer.
To succeed in the market, companies of any sector have to address the
marketing issues and tackle them adequately. Biotechnology-based companies are a
special case, because the strategic decisions and the marketing issues to handle may
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not be ordinary. In fact, due to the science based nature of the sector, most companies
are technology intensive, being often involved in the development of highly
innovative products within a new, still evolving, field, while struggling to gain a
leading edge over competitors in the market. Thus, companies have to leverage their
technological capabilities by selecting R&D projects that lead to a competitive
advantage (Siontorou and Batzias, 2010). Options cover mainly two major areas. The
first is selecting the pioneering posture, where a company, acting as a knowledge
creator, aims at introducing niche products and technologies into the market;
biotechnology being still strongly bound to basic research, readily available from
academia, makes innovation and novelty more feasible in comparison with other
traditional technology sectors, provided that a company can afford the high cost and
risk of extensive research, development, authorization and promotion; clearly, this
option can be sustained only by large multinational corporations. The second option is
choosing a combination of applied and basic research projects by using the company’s
internal and external R&D resources and building on already existing knowledge. The
former usually refer to in-house R&D activities, whereas the latter may include
purchasing or licensing of technology from other companies, or joining strategic
alliances to acquire that technology. Inter- and intra- technical/scientific knowledge
and competency may be disseminated in a variety of ways: patent disclosure,
publications, technical meetings, conversations between employees of the same or
competing companies, partnership within the same consortium that carries out a big
project, hiring of employees from rival companies and reverse engineering of
products (Nonaka et al., 1996; Luo et al., 2005; O’Connor and DeMartino, 2006).
Opportunities arise mainly from the specific, and often difficult to fulfill,
deficits of marketed products, requiring a lot of ‘speculative’ R&D, which is a
valuable path for strengthening the company’s position (Bernstein and Singh, 2006).
Depending on the company’s resources and capabilities, technological development
may start from advancing basic biochemical research on an early-stage candidate, or
from re-engineering/re-designing later stage products. The more basic the ‘product-tobe’ is, the higher the investment required and the uncertainty of the outcome, but the
more prominent becomes the differentiation of the product for gaining a satisfactory
market share.
When a biotechnology company seeks to define the balance between R&D in
established areas of corporate knowledge and speculative R&D for enhancing its
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innovative capability, a decision has to be made on how to prioritize investment.
Extensive research has been conducted over the last 30 years to produce methods for
improving project selection processes. The decision-making support methods relying
on artificial intelligence provide a systematic approach to the implementation of
computer-aided designs, which produces a final structure from initial specifications
(Sidiras and Koukios, 2004; Günay et al., 2008; Sidiras et al., 2011; Tashkova et al.,
2012). Notwithstanding, successful development and market acceptance still entails
high risk, especially at accelerated market entry strategies (Rodríguez-Pinto et al.,
2011). A versatile organization, with high degrees of freedom and ability (in both,
resources and sustainability) to risk taking, commonly supports the development of
breakthrough products and supports innovation, while balancing between having
projects that run efficiently according to plan and leaving room for exploration and
the creation of new knowledge that will initiate a new cycle of product development
in the future.
Ultimately, systems of innovations are created, that push and pull actors at
definite places, according to the needs of the innovator at first, but later of the system
itself (Gilsing and Nooteboom, 2006; Mittra et al., 2011). The inevitable universityindustry interplay is not always straightforward, owing to the different scopes and
strategies of each side. This works presents the challenges to knowledge exploitation,
using the case of biosensors as an exemplar for highlighting efficient knowledge
transfer mechanisms from lessons learnt.
2. Systems of Innovation
Innovation systems and science policies predominantly focus on linkages
between universities and industry, and the commercial translation of academic
discoveries. Overlooked in such analyses are the knowledge discovery processes
within academic research and the non-commercial aspects of science. The desire to
move research into practical applications in order to capture the benefits derived from
scientific discovery has long motivated science funding and program development,
especially in the biotechnology sector. Distinctions between ‘pure’ and ‘applied’
science were common during the 1960’s through the 1980’s. However, it is now
widely recognized that this linear model of innovation, in which pure science is
published in the academic literature, then picked-up by industry and developed into
useful applications, is unnecessarily simplistic and frequently inaccurate; more likely,
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scientific research contains aspects of both, basic inquiry (discovery) and practical
application (utility), and moves back and forth between the two (Stokes, 1997). These
movements take place at a deeper phenomenological level and their traces become
somehow evident only to researchers who actually incorporate instinctively the
relevant methodological paths into their hypotheses. On the other hand,
methodologists or technology managers, engaged with the theoretical or the practical
aspect, respectively, of research but not with the research itself, stay usually on a
surface level being able to discriminate only quasi-linear segments of a trace which is
rather complicate, sometimes tending to cover part of the surface like a Peano curve.
Bio-related academic research remains at the forefront of the scientific and
industrial infrastructure for two decades now, feeding a push-pull mechanism that
serves both, academic excellence and product innovation, through an efficient
technology transfer stream (Schmoch, 2007; Bishop et al., 2011). The impact of biotechnology on the pharmaceutical sector is a case in point . The innovative network
developed has not only altered the technological profile of the sector, by driving
chemical production to bioprocesses, but has also shifted drastically the trajectories
from chemistry towards life-sciences (Gilsing and Nooteboom, 2006). The network’s
grounding in basic research, together with its multidisciplinary and decentralized
dynamics, increased the relevance of academic research outputs, fostered the
emergence of specialized biotechnology firms that necessitated (and promoted) intraindustrial
cooperations
and
university-industry
links,
while
obliged
the
pharmaceutical firms to reposition their strategies (Salicrup and Fedorková, 2006;
Canongia, 2007). Markedly, the high transformative capacity of the science-enabled
technology, i.e., the high capability of the scientific bodies involved to constantly
redefine the portfolio of their products based on endogenously produced knowledge
(Garud and Nayyar, 1994), brought about significant structural and institutional
changes to the sector, giving rise to new scientific and technological opportunities.
There are cases, however, where several new technologies, incubated and
fostered within the same evolutionary frame of bio-sciences, had only an indirect,
supportive, and subsidiary impact and failed to challenge the sectoral system and its
established structures in any substantial way. Biosensor technology, for example, was
set off to revolutionize instrumentation and measurement (see e.g., Churchouse et al.,
1986; Griffiths and Hall, 1993), as well as medical diagnostics and treatment (see e.g.,
Mascini, 1992; Wilson et al., 1992). Yet, the industry was very reluctant to capitalize
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on the university-produced knowledge and innovation. That gave rise to a, mostly
European, paradox where long-term academic excellence has been sustained by a
large number of university-hosted biosensor groups with a high absorptive capacity,
i.e., a marked capability to recognize, value and assimilate exogenous technological
change within their scope of research (Siontorou and Batzias, 2010), yet low
translational capability, i.e., low commercialization of the work produced (Luong et
al., 2008).
3. Building the innovation hub: the case of glucose sensors
The pioneering work of Leland C. Clark, Jr. in 1960s that transformed an
oxygen probe into a glucose meter (Clark and Lyons, 1962), paved the way for the
evolution of biosensor revolution. The enzyme probe (Fig. 1) reached the market
almost a decade later, when Yellow Springs Instruments (YSI) launched its whole
blood glucose analyzer (Model 23 Glucose Analyzer) with a polarographic electrode
based on Clark’s patent (Clark, 1970). The various actors engaged in the emergent
techno-economic network, drew roadmaps to commercialization that shifted
drastically the trajectories of the biomedical industry (Churchouse et al., 1986).
Roadmapping, set at early 1980s (see, e.g., Severinghaus and Astrup, 1986), followed
a strong market-pull mechanism for clinical diagnostics and home-care over-thecounter glucose sensing. Three paths emerged, namely on glucose self-monitoring
devices, artificial pancreas and continuous (in vivo) glucose monitoring; the drivers
that created and sustained the paths were industry, society, and scientific community,
respectively, creating ad hoc an efficient thick innovation system, i.e., a sui generis
system consisting of actors with endogenous knowledge and interest on glucose
sensing, characterized, at least until 1990, by knowledge asymmetry and limited
dispersion around the early US biosensor research clusters. Inevitably, the concrete
structure of the network pushed and pulled players into finite sets of positions
(Siontorou and Batzias, 2010), according to the needs for knowledge absorption and
knowledge exploitation, patterning knowledge flows through the mechanisms of
demand-driven absorptive capacity and supply-driven transformative capacity,
respectively.
During 1970-1989, this mono-disciplinary glucose frame gained its structural
properties, outlining scopes, technology strategies, definitions, and classifications,
focusing on glucose monitoring (Heller and Feldman, 2008), although other possible
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and plausible fields of biosensing applications started timidly to emerge. Markedly, a
strong and effective university-industry alliance had been formed (Siontorou and
Batzias, 2010), mostly in US, engaging actors with similar awareness of the
technology at hand, aiming at solving rapidly scientific and technical problems (vastly
on accuracy and sample size, respectively) and at building capabilities in line with
customers’ requirements. Thereby, this productive coexistence of industry, science
and society (Fig. 2) outlined, inevitably, a strategic perspective within which the
companies strived to align their technology trajectories in order to sustain the
performance of their product concepts and enhance the existing knowledge base.
As more industries got involved, including Ames/Miles, Lifescan, Roche,
Boehringer Mannheim, Bayer, Medistron, and Johnson & Johnson, more researchers
got involved, lifting fast expectations from ex vivo measurements to in vivo
monitoring within less than a decade (Vadgama, 1984). Clearly, industry realized the
new scientific field as a discontinuous and disruptive technology at an era of ferment,
i.e., as an innovation system resulting in the generation of new technologies and in
changes in the relative weighting of existing technologies, presenting superior
performance trajectories along critical dimensions that customers (i.e., diabetic
patients) value. Larger firms struggled to take advantage of this technology ahead of
competitors in the market. Some have developed in-house R&D for capitalizing on
own architectural innovation (i.e., of the whole system and its components, according
to the definition of Carayiannis et al., 2003) and for extending the radical technology;
for example, after Roche launched the Haemo-Glucotest in 1968, the company spent
10 years on improving it and needed another ten years to launch its line of glucose
meters (Accu-Chek). Others tried to form direct links with the firms that had already
the technology at near to ‘fully fledged’ innovations, i.e., outsourcing knowledge
through acquisitions that enabled rapid market entries secured by proprietary rights;
for example, Bayer acquired Ames/Miles in 1978 and launched Glucometer® a few
years later, while Johnson & Johnson acquired Medistron and LifeScan in 1986 to get
hold of the Glucoscan®.
Gradually distancing itself from the university science-base, the industry
established a high competition arena at early 1990s that gave rise to significant
advances in technology, with miniaturization of analyzers and a remarkable increase
in the numbers of devices available, nowadays listed by the ‘ADA's Buyer's Guide to
Diabetes Products’. This arena was determined by the timing and impact of the
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innovation under consideration: the new technology was called to address fast the
needs of 5% of the US population and 0.4% of the world population, at an increasing
rate of 3-5% annually and at a direct health care cost of $2000 per patient (Steck and
Rewers, 2004). This innovation gave a straightforward cost reduction trajectory at a
performance level higher than that of the earlier processes that it cancelled out. Four
strategies have been consecutively followed in industrial research to deal with the
challenges and opportunities of diabetic management (Fig. 3), as determined by patent
searching for mapping scientific knowledge: (i) steepening of the slopes of the market
trajectories using marketing initiatives so that the performance improvements
demanded by the health care providers could be successfully addressed by the
industry (R&D on performance), (ii) ascending the trajectory of sustaining technology
into ever-higher tiers of the market (R&D on reliability), (iii) aligning with the needs
of end customers (R&D on convenience), and (iv) increasing the market share with
less costly formats and processes.
When the marketed technologies became comparable in most critical aspects,
the real clinical needs for detecting unsuspected hypoglycemia, neonatal screening,
and adolescence diabetes monitoring, urged the industry to look into the science pushpool for alternate testing, giving rise to minimally invasive glucose monitoring (Fig.
4). Knowing that this shifting was not enough to maintain the competitive advantage,
non-invasive ‘glucometry’ became rapidly the new target of the industries, enabling
the renewed intensification of university-industry relation seen today.
4. Lessons learnt from the knowledge-demand and knowledge supply interplay
Industrial biosensor advancements have been marked by a knowledge supplydemand tradeoff. The former, driven by availability of knowledge, supported
innovations during the 1990s, while the latter, derived from market needs, has just
started to evolve, especially in USA and Japan.
Biosensor research did not always paid out, so companies reduced patents
referring to applications other than glucose monitoring. Bio-Nano Sensium
Technologies, a company specializing in nanobiotechnology for biomedical devices,
has exclusive access to Toumaz Technology's patent portfolio. The increasing number
of patents over the last five years reflects the company's rapid progress in its scientific
discovery process toward offering advanced wireless biosensors.
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Researcher patents increased although very few have been managed to support
their claims or find their way to the market. Much of the work has been done in
secrecy by science based enterprises. At the early 1990s, research papers have
dramatically increased, prompting various universities and institutes to specialize first
in biosensor (academic spin off) and long after to provide biosensor courses. Some of
the researchers have turned to private companies and institutes to offer their insight
and experience. Biacore, for example, began operating in 1984, when expertise from
Pharmacia, Linköping Institute of Technology and the Swedish National Defense
Research Institute, were brought together with Biacore’s predecessor, Pharmacia
Biosensor AB. Approximately 65 million US$ has been invested in the development
of Biacore and the technology on which it is based. An initial research phase which
essentially included development of the fundamental affinity-based biosensor
technology comprising surface chemistry, flow systems and optical detection methods
was completed in 1989. The result was Biacore®, a biosensor-based analytical
instrument for studying molecular interactions, launched in 1990. Further products
followed, all based on surface plasmon resonance technology. The business evolved
into a largely independent commercial enterprise, which posted its first profit in 1994.
Another example of know-how transfer from academics to industry is the
establishment of Agamatrix in 2001 from the collaboration between S. Vu, an expert
in machine-learning algorithms, and S. Iyengar who has just finished his PhD in
biosensors at Cambridge University.
A considerable increase of research funding for biosensors has been seen in
2004, along with new lines of funding for technology demonstrators and industrial
investment in prototype systems. This can boost the supply side, provided that
universities will be encouraged to support the area. This presupposes liberation of
university staff time for R&D, longer term funding for individuals who can
demonstrate the quality of their work and its relevance to user requirements, as well
as improved support for graduates. For example, the alcohol wristwatch (Dummet et
al., 2008) has been developed through a collaboration between the University of
Southern California and the Brown University Medical School, investing largely on
simulation of alcohol metabolism (Fig. 5).
The demand side, on the other hand, has proved to provide a strict marketorientation as well as resources necessary for successful commercialization. Most
companies, however, realizing the huge cost of research, have decided to collaborate
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with academics, mostly on a short-term basis, although permanent collaborations do
exist. In 1996, Bioacore acquired control of EBI Sensors Inc.; this acquisition gave
Biacore access to fiber optic sensor technology developed by the University of
Washington (USA) and of which EBI is the exclusive worldwide licensee.
International scientific projects have also led to the involvement of the private
sector to research, as for example, the development of the biosensor for food born
bacteria from the Georgia Research Tech Institute. As there are still ineffective links
between research base and the end-user, mechanisms to foster more effective
technology transfer and better communication of technological opportunities and user
requirements should be established, in order for biosensors to grasp the opportunities
ahead.
The global market competition has been started to take shape: major
breakthroughs come from countries with strong infrastructures for microsystems
technology (MST), especially in the interface between biotechnology and
nanotechnology. For example, Germany has growing strengths and capabilities in
both fields. The group developed AWACCS and its predecessor RIANA (Tschmelak
et al., 2005a; 2005b), has a long history back on chemical sensors and multi-analyte
detection by artificial nose and ear devices. Groups from US and Japan are also
starting to focus major research efforts on these interfaces. Competition is also
emerging from Taiwan, Korea, Singapore and China, rendering this a hotly contested
field. products.
5. Concluding remarks
The key issues emerging from the experience gained in biosensor progress and
development, taken as a representative example of university-fostered biotechnology
revolution, refer to early information transfer and utilization, product innovation,
product quality, and efficient collaboration. Commercialization and research progress
entails the capability of research institutions and organizations to introduce new
concepts, products and features. The fast pace of technological change and the market
demands for novel and better products requires continuous innovation and fast market
introduction. This implies primarily a market-targeted research strategy, focusing on
needs requiring attention and not on substitution of niche technologies, especially on
the misjustification of cost-effectiveness. Progress should be quick to assimilate sidetechnological advancements as these become available. The momentum increase
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largely relies upon information flow, which is expected to benefit largely from
concurrent engineering practices
Technology perfection will not be enough to assure biotechnology success in
the near future, however. Successful products will have to fulfill unmet market needs
at a highly competitive arena. The successful biotechnology marketer, therefore, will
have to come up with convincing evidence to prove that his/her system has a clear
advantage over existing, well-known and public-accepted technologies.
Cross-functional teams provide an avenue for constituents to express concerns
and a mechanism for capturing learning. Early involvement empowers downstream
participants; they have a say before decisions are finalized. Simultaneous planning of
product, process, and manufacturing allows issues of manufacturability to be
evaluated and incorporated in the final product design. This approach affords a group
a stream of integrative innovations that may improve the value of the end-product,
enhance quality, and reduce development cost. With early release of information,
engineers can begin working on different phases of product development process
while final designs are evolving. The early release of information reduces uncertainty
and promotes the early detection of problems, which enables groups to avoid timeconsuming changes.
Moreover, the economic aspects of biotechnology are not been taken into
account when research strategies are considered. For the immediate future,
nanotechnology could be still expensive. If high volumes and low-cost products are
achieved, the markets could be huge. The question is whether the increased capability
of nano-products will be sufficient to open up large markets quickly, and thus
engendering a rapid decrease in costs. A related question is whether there will be
scope for small firms to invest on nanotechnology.
References
Bernstein, B. and Singh, P.J. (2006). An integrated innovation process model based
on practices of australian biotechnology firms. Technovation 26, 561-572.
Bishop K., D’Este P., Neely A. (2011). Gaining from interactions with universities:
Multiple methods for nurturing absorptive capacity. Research Policy 40, 30-40.
Canongia C. (2007). Synergy between competitive intelligence (CI), knowledge
management (KM) and technological foresight (TF) as a strategic model of
Page 13
prospecting — The use of biotechnology in the development of drugs against
breast cancer. Biotechnology Advances 25, 57-74.
Carayannis E.G., Gonzalez E., Wetter J. (2003). The nature and dynamics of
discontinuous and disruptive innovations from a learning and knowledge
management perspective. In: Shavinina L, editor. The international handbook
on innovation. Elsevier, pp. 115-138.
Chataway, J., Tait, J. and Wield, D. (2004). Understanding company r&d strategies in
agro-biotechnology: Trajectories and blind spots. Research Policy 33, 10411057.
Churchouse S.J., Battersby C.M., Mullen W.H., Vadgama P.M. (1986). Needle
enzyme electrodes for biological studies. Biosensors 2, 325-42.
Clark
L.C.
Membrane
polarographic
electrode
system
and
method
with
electrochemical compensation. US patent no. 3,539,455 Nov. 10, 1970.
Clark L.C., Lyons C. (1962). Electrode systems for continuous monitoring in
cardiovascular surgery. Annals of New York Academy of Science 102, 29-45.
Dumett M.A., Rosen I.G., Sabat J., Shaman A., Tempelman L., Wang C., Swift R.M.
(2008). Deconvolving an estimate of breath measured blood alcohol
concentration from biosensor collected transdermal ethanol data. Applied
Mathematics and Computation 196, 724-743.
Ernst & Young (2011). Beyond borders - Global biotechnology report 2011. Funding
biotech projects. Available at http://www.ey.com.
Ernst & Young (209). The 2009 business risk report: top 10 business risks affecting
life sciences companies. Available at http://www.ey.com.
Fumento, M. (2003). BioEvolution: How biotechnology is changing our world.
Encounter Books.
Garud R., Nayyar P.R. (1994). Transformative capacity: continual structuring by
intertemporal technology transfer. Strategic Management Journal 27, 365-85.
Gilsing V., Nooteboom B. (2006). Exploration and exploitation in innovation
systems: The case of pharmaceutical biotechnology. Research Policy 35, 1-23.
Griffiths D., Hall G. (1993). Biosensors — what real progress is being made? Trends
in Biotechnology 11, 122-30.
Günay M.E., Nikerel I.E., Oner E.T., Kirdar B., Yildirim R. (2008). Simultaneous
modeling of enzyme production and biomass growth in recombinant
Page 14
Escherichia coli using artificial neural networks. Biochemical Engineering
Journal 42, 329-335.
Heller A., Feldman B.. (2008). Electrochemical Glucose Sensors and Their
Applications in Diabetes Management. Chemical Reviews 108, 2482-505.
Luo L., Kannan P.K., Besharati, B. and Azarm S. (2005). Design of robust new
products under variability: Marketing meets design. The Journal of Product
Innovation Management 22, 177-192.
Luong J.H.T., Male K.B., Glennon J.D. (2008). Biosensor technology: Technology
push versus market. Biotechnology Advances 26, 492-500.
Mascini M. (1992). Biosensors for medical applications. Sensors and Actuators BChemical 6, 79-82.
Mittra J., Tait J., Wield D. (2011). From maturity to value-added innovation: lessons
from the pharmaceutical and agro-biotechnology industries. Trends in
Biotechnology 29, 105-109.
Nonaka, I., Umemoto, K. and Senoo, D. (1996). From information processing to
knowledge creation: A paradigm shift in business management. Technology in
Society 18, 203-218.
O’Connor, G.C. and DeMartino, R. (2006). Organizing for radical innovation: An
exploratory study of the structural aspects of RI management systems in large
established firms. The Journal of Product Innovation Management 23, 475-497.
Rodríguez-Pinto J., Carbonell P., Rodríguez-Escudero A.I. (2011). Speed or quality?
How the order of market entry influences the relationship between market
orientation and new product performance. International Journal of Research in
Marketing 28, 145-154.
Salicrup L.A., Fedorková L. (2006). Challenges and opportunities for enhancing
biotechnology and technology transfer in developing countries. Biotechnology
Advances 24, 69-79.
Schmoch U. (2007). Double-boom cycles and the comeback of science-push and
market-pull. Research Policy 36, 1000-1015.
Severinghaus J.W., Astrup P.B. (1986). History of blood gas analysis. IV. Leland
Clark's oxygen electrode. Journal of Clinical Monitoring 2, 125-39.
Sidiras D., Batzias F., Ranjan R., Tsapatsis M. (2011). Simulation and optimization of
batch autohydrolysis of wheat straw to monosaccharides and oligosaccharides.
Bioresource Technology 102, 10486-10492.
Page 15
Sidiras D.K., Koukios E.G. (2004). Solar systems diffusion in local markets. Energy
Policy 32, 2007-2018.
Siontorou C.G., Batzias F.A. (2010). Innovation in biotechnology: moving from
academic research to product development—The case of biosensors. Critical
Reviews in Biotechnology 30, 79-98.
Steck A.K., Rewers M.J. (2004). Epidemiology and geography of type 1 diabetes
mellitus. In: DeFronzo RA, Ferrannini E, Keen H, Zimmet P, editors.
International textbook of diabetes mellitus. West Sussex: John Wiley & Sons
Ltd., pp. 15–32.
Stokes D.E. (1997). Pasteur’s quadrant: Basic science and technological innovation.
Washington DC: Brookings Institute Press.
Tashkova K., Šilc J., Atanasova N., Džeroski S. (2012). Parameter estimation in a
nonlinear dynamic model of an aquatic ecosystem with meta-heuristic
optimization. Ecological Modelling 226, 36-61.
Thompson, E. and Vonortas, N.S. (2005). Biotechnology evolution and regulation of
pharmaceuticals. In: R. Carruth, Ed., Global governance of the pharmaceuticals
industries: Transatlantic and trilateral
regulatory harmonization
multilateral policy cooperation for drug safety.
and
The George Washington
University, Ch. 9.
Tschmelak J., Proll G., Riedt J., Kaiser J., Kraemmer P., Bárzaga L., et al. (2005a).
Automated Water Analyser Computer Supported System (AWACSS) Part I:
project objectives, basic technology, immunoassay development, software
design and networking, Biosensors and Bioelectronics 20, 1499-1508.
Tschmelak J., Proll G., Riedt J., Kaiser J., Kraemmer P., Bárzaga L., et al. (2005b).
Automated Water Analyser Computer Supported System (AWACSS): Part II:
intelligent,
remote-controlled,
cost-effective,
on-line,
water-monitoring
measurement system, Biosensors and Bioelectronics 20, 1509-1519.
Vadgama P. (1984). Enzyme electrodes for continuous in-vivo monitoring. TrAC –
Trends in Analytical Chemistry 3, 13-6.
Wilson G.S., Zhang Y., Reach G., Moatti-Sirat D., Poltout V., Thévenot D.R., et al.
(1992). Progress toward the development of an implantable sensor for glucose.
Clinical Chemistry 38, 1613-7.
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Figure 1. Schematic of the glucose probe manufactured by YSI Inc. that later termed
‘first generation’ biosensor system.
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biosensor proof-of-concept
recruitment
and training
sources and
new ideas
fundamental
understanding
of sources
adsorption of
external knowledge
problem solving
innovation system
knowledge
dissemination
and transfer
transformation of
knowledge to
university
industrial
collaborations
technology
knowledge
interpretation
awareness and
acquisition of
knowledge
biosensor concept
Figure 2. The glucose biosensor dynamic innovation system
Figure 3. The glucose sensor technology performance roadmap referring mainly to
the ‘context’ of innovation, i.e., the socio-technical perspective drawn by the industry
(via a cost reduction trajectory) on the basis of the environment in which the
innovation emerged, and the effect of that environment on the technology evolution
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invasive glucose monitoring
lab-on-chip technology
fluorescence
low precision
infrared
strong scattering
laser
reflectance
poor ruggedness
impedance spectroscopy
coulometric
electrochemical
Precision Xtra Abbot Inc.
Breeze 2
Contour Bayer
minimally invasive glucose monitoring
amperometric tear sensor
sensor-based microdialysis
Glucoday
thin film holographic
optical sensor
sub-cutaneous sensor
Menarini Diagnostics
STS
CGMS Medtronic Minimed
Guardian REAL-time
SCGM Roche Diagnostics
Dexcom Inc.
Freestyle navigator Abbot Inc.
Paradigm REAL-time
trained user
time-lag
reverse iontophoresis
bulky configuration
frequent time-lag
local
calibration
inflammatory
reaction
moderate
precision
GlucoWatch Biographer Animas Corp.
GlucoWatch G2 Biographer
low accuracy
time-lag
high false rates
long-term sensor system
skin irritation
LTSS Minimed-Medtronic
non- invasive glucose monitoring
optical coherence tomography
near infrared spectroscopy
GluControl GC300 ArithMed GmbH
Diasensor
high false
rate
nonspecific
low ruggedness
Bico In.
mid infrared spectroscopy
OrSense orSense Ltd.
SugarTrac LifeTrac Systems Inc.
raman spectroscopy
TouchTrak Samsung Ltd.
temperature-modulated
localised reflectance
non-specific
low precision
poor penetration
lag time
high false rate
low precision
interference
instability
impedance spectroscopy
Pendra Pendragon Medical Ltd
low
reliability
ocular
spectroscopy
polarization changes
Glucoband Calisto medical Inc.
scattering
ultrasound
technology
error-prone
poor specificity
electromagnetic sensing
GlucoTrack Integrity Appl. Ltd.
low precision
thermal
spectroscopy
moderate
precision
interference
background
noise
poor ruggedness
poor patient
convenience
Aprise Gluco Inc.
low
scientific
clarity
low precision
lag time
poor
ruggedness
low manufacturability
non-specific
instability
Hitachi Hitachi. Ltd.
low precision
poor ruggedness
iontophoresis
KMH Co Ltd.
GluCall
Sysmex Sysmec Corp.
skin irriation
lag time poor ruggedness
low sensitivity
performance/quality constraints
Figure 4. The three phases of glucose monitoring, invasive, minimally invasive and
non-invasive, represented industrial efforts to produce devices according to
specifications set by the intermediate customers (health care providers) and the endusers (patients). Each phase apprehended available (at the time) technologies, each
with drawbacks that pushed the path forward. Interestingly, R&D was not about
improving performance and/or minimizing faults but kept focusing on the next phase
generation and moving fast between technology platforms in order to get ahead of
competition. The last phase is clearly dependent upon university research, setting off
again strong pull mechanisms and high speed information flow patterns.
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development of algorithm for sensor
software development
calibration
University of Southern California,
Department of Applied Mathematics
development of biochemical model of
alcohol metabolism
University of Southern California,
Department of Applied Physics
Brown University Medical School
Department of Psychology
development of biosensor for in vivo
sensor design and
monitoring of alcohol metabolism
calibration of biosensor on a per
subject basis
production
clinical data on alcohol metablism
model evaluation
prototype development:
wristwatch biosensor to track
alcohol use
Figure 5. The development of the alcohol wristwatch has been realized through an
inter-university cooperation under a governmental financial framework addressed to
the Psychology Department of Brown University for controlling alcoholism.