Summary slides and articles
Mangement Life Sciences
Innovations
Course aims
At the end of the course, students will be able to:
- Recapitulate insights in various aspects of the management of innovation processes in the
life sciences.
- Analyze developments in life sciences innovation processes by applying specific innovation
theories.
- Reflect on the extent to which innovation theories can be used to analyze innovations in the
life sciences.
- Communicate the results of the analysis of life sciences innovations by means of a paper.
Course objectives
- Recap insights into diverse aspects of management of innovation processes in the life
sciences
- Analyze developments in life sciences innovation processes by applying specific innovation
theories
- Reflect on the extent to which innovation theories are useful to analyse life sciences
innovations
- Communicate about results of these analyses in a paper and presentation
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,Lecture 1: Course introduction and introduction to ‘science-based
innovation’
In this lecture, an introduction is given to the profession and we will discuss the type of companies
and innovation processes that are central to this course. This mainly concerns science-based
companies; high-tech companies that create value through their in-house R&D and/or through
bringing in knowledge from research institutions.
Lecture 1: Introduction MLSI
Are innovation proccesses the same in every sector?
And why is innovation in life sciences different (or not)?
What do we exactly mean with ‘life sciences’? and with biotechnology?
Definition biotechnology
‘’Biotechnology is defined as the application of science and
technology to living organisms as well as parts, products and
models thereof, to alter living or non-living materials for the
production of knowledge, goods and services’’ (OECD, 2001).
Defintion ‘Life sciences’
- Scientific field
- Living organisms
- Ranging from micro-organisms to population ecology
- Molecular biology/biotechnology is one of the subfields
- Connection with industry
Emerging technologies in care
For example: digital care at a distance, big data, artificial intelligence, robotics, drones, 3D printing of
biomaterials, biotech, etc.
Life sciences sector is broad.
Management of Life Sciences Innovations
Life sciences innovations: science-based business
How does the ecology of the life sciences sector look like
Science-based enterprise
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, - LS/Biotech firm: fusion of science and business
- Create science and capture value from it
- Science is product of a firm’s activity
Pavitt (1984) taxonomy
Science-based firms:
- Basic science is pervasive
- R&D is key activity
- Inside & outside firm
- Appropiation
Between science and market
Sciences success … business not
Changing role of universities
- Science becomes a business
- Technology transfer; ‘valorization’
Anatomy of sector
- Roles and strategies of participants
- Institutional arrangements
- Rules of governance
Innovating in large life sciences companies
Munos, B. (2009). Lessons from 60 years of pharmaceutical innovation.
Despite unprecedented investment in pharmaceutical research and development (R&D), the number
of new drugs approved by the US Food and Drug Administration (FDA) remains low. To help
understand this conundrum, this article investigates the record of pharmaceutical innovation by
analysing data on the companies that introduced the ~1,200 new drugs that have been approved by
the FDA since 1950. This analysis shows that the new-drug output from pharmaceutical companies in
this period has essentially been constant, and remains so despite the attempts to increase it. This
suggests that, contrary to common perception, the new-drug output is not depressed, but may
simply reflect the limitations of the current R&D model. The implications of these findings and
options to achieve sustainability for the pharmaceutical industry are discussed.
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, Conclusion:
In the past 60 years, the pharmaceutical industry has delivered over 1,220 new drugs that have
played an important part in improving public health and extending life expectancy by an average of 2
months each year. The R&D model that has powered that success, however, is showing signs of
fatigue: costs are skyrocketing, breakthrough innovation is ebbing, competition is intense and sales
growth is flattening. This cluster of symptoms has often foretold major disruption in other industries.
Their experiences show that industries can survive such upheavals; someone always finds a way to
redesign the business model, but that someone, ominously, has seldom been an incumbent.
Could pharmaceuticals be different? Drug research today is the locus of many interesting
experiments that have the potential to rejuvenate the R&D model. Many of them are taking place in
areas that have traditionally been overlooked by the large companies, such as neglected diseases and
biodefence, which is consistent with the predictions of clayton christensen. Nevertheless, large
companies have also sponsored some highly innovative concepts, some of which are highlighted in
the previous section. However, although these experiments are proceeding, the industry is
increasingly caught in a pincer between an NME output that is essentially linear, and likely to remain
so, and a cost of producing NMEs that is increasing exponentially. At some point, the situation will
become untenable. This could tempt investors to force wholesale change onto the industry, unless
the industry pre-empts them with radical initiatives.
Scannell, J. W., Blanckley, A., Boldon, H., & Warrington, B. (2012).
Diagnosing the decline in pharmaceutical R&D efficiency.
The past 60 years have seen huge advances in many of the scientific, technological and managerial
factors that should tend to raise the efficiency of commercial drug research and development (R&D).
Yet the number of new drugs approved per billion US dollars spent on R&D has halved roughly every
9 years since 1950, falling around 80-fold in inflation-adjusted terms. There have been many
proposed solutions to the problem of declining R&D efficiency. However, their apparent lack of
impact so far and the contrast between improving inputs and declining output in terms of the
number of new drugs make it sensible to ask whether the underlying problems have been correctly
diagnosed. Here, we discuss four factors that we consider to be primary causes, which we call the
‘better than the Beatles’ problem; the ‘cautious regulator’ problem; the ‘throw money at it’
tendency; and the ‘basic research–brute force’ bias. Our aim is to provoke a more systematic analysis
of the causes of the decline in R&D efficiency.
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