Samenvatting
Summary of the MLSI lectures - Exam
Notes to all the MLSI lectures in preparation for the exam
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Voorbeeld 3 van de 24 pagina's
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Voorbeeld van de inhoud
Lecture notes MLSILecture 1 – Course introduction and introduction to ‘science-based
innovation’
Specific aspects to LS innovation processes that are peculiar to this field regulations/regulatory
approvals/ethics, very much based on (rapidly changing) scientific advancements, etc.
Life sciences & biotechnology:
Vast array of studies that focus on (organic) life (LS elements on all kinds of scales: organisms, parts
of organisms, bigger ecologies etc.) and biology; companies, human consumptions and their
influences on life and society; the incorporation/application areas of life science innovations in
society (foods, pharmaceutical, industrial etc.); 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)
- Scientific field
- Living organisms
- Ranging from micro-organisms to population ecology
- Molecular biology/biotechnology is one of the subfields
- Involves different sub-segments (e.g. providers, distributors, payers etc.)
- Connection with industry
Red biotechnology: application of biotechnology in human healthcare (e.g. bio pharmacy)
Green biotechnology: application of biotechnology in food and agriculture
White biotechnology: application of biotechnology for industrial purposes
More innovation management literature on red biotechnology (vs. green and white biotechnology):
- There is more (corporate) data available on red biotechnology
- In terms of innovation dynamics, red biotechnology is similar to green and white
biotechnology
Emerging technologies in the LS sector (in care) among others:
- Digital care at distance
- Robotics
- Artificial Intelligence
- Reality technology
- Shift to consumer market and DIY
- Wearables
- Regenerative medicine
- Gene modification
The wide variety of innovations and the (peculiar) LS characteristics, regulations/institutions
(e.g. supporting agencies) in place make managing (these fluid) innovations in LS important
and challenging
1
,LS innovations: science-based enterprises
- LS/Biotech firm: fusion of science and business
- Create science and capture value from it
- Science is product of a firm’s activity
- Basic science (R&D) is pervasive in the firm
- R&D is key activity
- Inside and outside firm
- Appropriation (protect and capture value from knowledge production e.g. through patents)
- Bridge between science and market
Life science firms are often science-based, which is also peculiar to the LS sector
Relationships between science and business:
- Scientific advancements/successes do not automatically lead to innovation/business success
- Co-evolution of science and business
- Science becomes a business
- Technology/knowledge transfers and valorization by universities to companies (changing
role of universities)
Anatomy of sector:
- Roles and strategies of participants
- Institutional arrangements
- Rules of governance
Lecture 2 – Large companies
Innovation in big pharma
Innovative in terms of the way big pharma companies present themselves (marketing and PR) and
do R&D internally, however they sometimes acquire innovative ideas through acquiring/buying ideas
from smaller companies/startups. Additionally, strong innovation labs within companies play an
import role for innovation. Innovative ideas/internal structures that stimulate innovation are also
mimicked across sectors when proven successful.
2
, Effectiveness of big pharma
Proposed problem: effectiveness of R&D/innovation within the pharma industry is in decline, while
R&D expenditure has been increasing (when examining the aggregate of all pharma companies)
less/a constant number of NMEs (new molecular/chemical entities) and BLAs (biologics license
applications) are/is being approved for going on the market; there is an average annual output.
- However, a critical remark to this point are that me too-drugs/less radical entries are not
taken into account not all new entries are thus very ‘innovative’. Also, new technologies
(e.g. gene drive etc.) take a lot of time to research and develop. Moreover, because of an
‘innovation peak’ innovation seems to be declining, whereas there is an overall steady
output/running average.
- Munos, 2009: Successful companies (on the left) that do science and R&D themselves can
better translate findings into their innovations (produce more successful entries), whereas
companies on the right side that rely heavily on mergers and acquisitions (M&A) produce
less successful entries the proposed problem is less present when examining individual
companies
Growth imperative of large companies: large firms are expected to grow by increasing outputs and
thus sales annually to ensure the growth imperative, innovation at constant speed is required.
Also, blockbusters can stimulate and result in growth
- Constant pressure for large companies to innovative in order to keep up with the growth
imperative
- Blockbusters run into patent cliffs as patents expire and drugs become generic as time
progresses
Causes of the proposed problem:
1. Science input problem: low hanging fruit problem and better than the Beatles problem
more difficult to be better than an already existing drug/product on the market (therapeutic
added value is difficult to achieve), thus in order to be successful and become more
innovative, new (research) areas could be discovered and exploited (connected to being a
first mover, however this is a high risk, high gain strategy)
2. Cautious regulator problem: stricter regulation can prohibit the output. However, it can also
drive innovation (level playing ground notion: everyone needs to adhere to the same kind of
regulation, which provides clarity and steer innovation)
3. Throw money at it problem: investments do not necessarily lead to returns
4. Basic research-brute force problem: investing in automating/industrializing the discovery
process of the R&D process does not always lead to successful outputs (big data approach),
as effort is not directed towards understanding the product and causal links
restructuring/reorganizing the R&D process, collocating skillsets
3
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