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|Document Type: ||Thesis or dissertation|
|Title: ||Predictive performance of front-loaded experimentation strategies in pharmaceutical discovery: a Bayesian perspective|
|Authors: ||van Dyck, Walter|
|Supervisors: ||Allen, Peter M.|
|Issue Date: ||2004|
|Abstract: ||Experimentation is a significant innovation process activity and its design is
fundamental to the learning and knowledge build-up process. Front-loaded
experimentation is known as a strategy seeking to improve innovation process
performance; by exploiting early information to spot and solve problems as upstream as
possible, costly overruns in subsequent product development are avoided. Although the
value of search through front-loaded experimentation in complex and novel
environments is recognized, the phenomenon has not been studied in the highly relevant
pharmaceutical R&D context, where typically lots of drug candidates get killed very
late in the innovation process when potential problems are insufficiently anticipated upfront.
In pharmaceutical research the initial problem is to discover a “drug-like”
complex biological or chemical system that has the potential to affect a biological target
on a disease pathway. My case study evidence found that the discovery process is
managed through a front-loaded experimentation strategy. The research team gradually
builds a mental model of the drug’s action in which the solution of critical design
problems can be initiated at various moments in the innovation process.
The purpose of this research was to evaluate the predictive performance of frontloaded
experimentation strategies in the discovery process. Because predictive
performance necessitates conditional probability thinking, a Bayesian methodology is
proposed and a rationale is given to develop research propositions using Monte Carlo
simulation. An adaptive system paradigm, then, is the basis for designing the simulation model used for top-down theory development.
My simulation results indicate that front-loaded strategies in a pharmaceutical
discovery context outperform other strategies on positive predictive performance. Frontloaded
strategies therefore increase the odds for compounds succeeding subsequent
development testing, provided they were found positive in discovery. Also, increasing
the number of parallel concept explorations in discovery influences significantly the
negative predictive performance of experimentation strategies, reducing the probability
of missed opportunities in development. These results are shown to be robust for
varying degrees of predictability of the discovery process.
The counterintuitive business implication of my research findings is that the key
to further reduce spend and overruns in pharmaceutical development is to be found in
discovery, where efforts to better understand drug candidates lead to higher success rates later in the innovation process.|
|Appears in Collections:||PhD, DBA, and MSc by Research theses (School of Management)|
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