Paper to be presented at the DRUID Summer Conference 2006 on
KNOWLEDGE, INNOVATION AND COMPETITIVENESS:
DYNAMICS OF
FIRMS, NETWORKS, REGIONS AND INSTITUTIONS
Copenhagen, Denmark, June 18-20, 2006
G: Corporate Innovation, Strategy and Organization
LEARNING FOR INNOVATION IN SCIENCE-BASED INDUSTRIES:
THE CASE OF PHARMACEUTICAL CRUG DISCOVERY
Danielle D. Dunne
PhD Candidate
ddunne@andromeda.rutgers.edu
Deborah Dougherty
Professor
doughert@rbsmail.rutgers.edu
Department of Management and Global Business
Rutgers Business School
Rutgers, the State University of New Jersey
111 Washington St.
Newark, NJ 07102
Initial draft, January 9, 2006
Abstract
While science differs from engineering, most learning models for innovation come from engineeringbased industries. We examine learning in pharmaceutical R&D to explore the industry’s declining R&D
productivity, and to develop new theory for science-based R&D learning. We find that learning methods
of inquiry, integration, and sensemaking in drug discovery are similar to those in academic science in that
there are no paths or physical frameworks to fill in, but are much more socially interactive. The method of
inquiry is searching for clues (not for causes as in engineering), and involves a tenuous, stepwise process
of searching the multi-dimensional problem space of human disease. Scientists work like detectives, not
engineers, noticing, gathering, and digging for clues. Integration occurs by iterating among specialties to
combine clues into temporary and partial patterns. Rather than brokering, iterating helps to select among
clues and “round them up” into “bodies of data and evidence” around a hypothesis that a given molecule
will solve a medical problem. Sensemaking involves all senses, not just hands, is both cerebral and
unconscious, and is situated in the overall social organization of drug discovery. The organization
becomes the main sensemaking organ for this innovation process. We develop implications for
managing the special challenges of accumulation that science-based learning produces (processes lead
easily to knowledge depreciation, not accumulation), for weaving engineering solutions into the process
more appropriately (when technologies are used in engineering mode, as they often are, they do not
enhance learning), and for organizing to enable this expanded sensemaking.
Keywords: Learning, science, pharmaceuticals
JEL – codes: L65, O31
1
Support for this research is provided by the MINE program (Managing Innovation in the New
Economy), Roger Miller, PI, Ecole Polytechnique, Montreal, Quebec, Canada; The Technology
Research Management Center of Rutgers Business School, Department of Management and
Global Business; and RBS funding from the research resources committee.
2
It is well established that the work of science differs fundamentally from the work of engineering
(Bunge 1967; Vincent 1990; Cardinal and Hatfield 2000; Floricel and Dougherty 2006).
Scientists seek to generate a fuller understanding of natural phenomena, while technologists seek
to apply knowledge in useful products or processes. Studies show that science and technology
progress independently, each building upon its own prior developments (Allen 1977). It stands
to reason, therefore, that people engaged in innovation might learn very differently in sciencebased industries than in engineering based industries. But our understanding of learning and its
management for innovation is based largely on engineering industries such as automobiles,
electronics, computing, and hard goods production (Clark and Fujimoto 1991; Brown and
Eisenhardt 1997; Carlile 2004). The purpose of this paper is to tease out the learning processes
underlying pharmaceutical drug discovery, innovation in a science-based industry. Clarifying
the unique learning processes in this industry is vital for two reasons. First, pharmaceutical
R&D productivity has declined precipitously in recent years, and in fact evinces a negative
learning curve – costs have gone way up while output has gone way down. Global R&D
expenditures have increased from $30 to $60 billon from 1993 to 2005, while output of new
molecular entities (NMEs), or drugs, has dropped from around 40 to 25 in that period
(Economist June 18 2005). Second, more fully exploring learning in this industry can help build
general theory of scientific learning, since 21st century industrial innovation may increasingly
rely on scientific understandings, not just on engineering (Grove 2005).
This paper provides a preliminary report of grounded theory on learning in drug
discovery based on interviews with 57 scientists and managers in seven “big pharma” firms, and
in three smaller biotechnology firms that sell technologies to “big pharma.” Our emerging
results suggest new applications of existing R&D management insights in the pharmaceutical
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industry, and new ideas from that industry for R&D learning. We focus on the general process
of small molecule drug discovery that occurs in “big pharma” companies, which is similar across
these firms. The companies do vary by culture, disease emphasis, and how they organize the
numerous steps in this process. But all are trying to improve productivity with cross-disciplinary
teams organized by therapy area, and holding these groups accountable for larger chunks of the
process. Indeed, the era of the star scientist, if it ever existed in fact, is fading as innovation in
this industry is becoming a “team sport” (cf Tushman and O’Reilly 1997) just like it has become
in most other industries.
We find three science-based processes of learning in drug discovery that differ
qualitatively from engineering learning. Before presenting our findings and developing their
implications for managing learning in this industry, we describe the learning challenges in drug
discovery and the unique problems of working with the still largely unknown arena of human
biology and disease. Many possible causes for the productivity decline in pharmaceuticals may
exist, among these national health care crises, regulatory pressures, difficulties managing the
multi-firm and global nature of the process, and the inertia that can arise in large, historically
very profitable firms. We limit our attention to learning challenges in the drug discovery,
recognizing that these challenges interact with other pressures. We then outline how learning in
engineering differs from that in science to provide a template against which we will compare our
interview data on drug discovery. Grounded theory building depends on comparison to surface
defining properties and dimensions that distinguish activities being examined.
4
CONCEPTUAL BACKGROUND
Pharmaceutical Drug Discovery in Context
That significant learning challenges exist in drug discovery is revealed from three
different perspectives: the sheer complexity of the process; the failure, so far, for major scientific
and technological breakthroughs to actually enhance drug discovery; and the limited
productivity, in terms of profits, of the biotechnology industry that supports drug discovery.
First, developing new drugs comprises the creation and evaluation of medicines for the safe and
effective treatment of human disease. Figure 1 outlines the key activities of the entire process,
from target discovery to the evaluation of safety and efficacy over the long term, post launch.
The total process averages 12 years until market launch, with enormous attrition rates: 10,000
compounds are initially screened, 250 enter preclinical testing, 5 of these enter clinical trials, and
only 1 on average gets FDA approval. We focus on the first six key activities in figure 1,
typically referred to as “discovery,” which takes 6 years on average (varying from 2-10 years).
The activities across the entire process are interdependent and cannot be separated simply, since,
for example, new “targets” are not fully validated until drugs are on the market long enough to
discern side effects. As well, drugs developed for one reason are often found to work for another
problem late in the process (e.g., erectile dysfunction medicines and statins were initially
developed for cardiovascular problems). However, the transition to clinical trials represents a
shift in emphasis, since different people and regulations are involved, and since costs ramp up
rapidly (clinical trials are very expensive). The Appendix provides a synopsis of drug discovery,
and explains the key activities in this process.
As figure 1 and the Appendix indicate, the key activities of drug discovery involve
extensive scientific learning, as possible pathways for a disease are discovered and sorted out,
5
the roles of various proteins in those pathways are discerned and verified, molecular compounds
which might bind to those proteins (called “targets”) are discovered and examined for toxicity
(e.g., do they also bind to other proteins, thus causing side effects) and efficacy (e.g., how are
they absorbed, metabolized and excreted by the body). The subject matter of drug discovery –
the human body – is not only enormously complex but also largely unknown. One scientist we
interviewed explained that a biological system has been “tweaked” over billions of years of
evolution, so while one thing has to work in concert with another to get a function, like in
engineering, there are many more regulatory levels, massive redundancies, and systems overlaps.
Another scientist contrasted what he learned in a two day executive education program on
reducing cycle time with what goes on in pharmaceuticals:
It was interesting in a sense. They look at it in somewhat of a linear conception and have
prototypes. Basically it is a linear process and at the end you have a physical product that
is governed by physical laws, and can be modeled. That does not fit with us…. Our
process is highly iterative, and the scientist goes back several times before he comes up
with a chemical entity. It is biology to understand how the body works and chemistry to
design a compound, but we don’t know how it will talk to the body… Biology is not
linear, it is all a network. There are multiple channels for arriving at a disease…
The linearity, adherence to physical laws, and ability to be modeled distinguish the subject
matter of engineering in general from that in life science. Indeed, since drugs are by definition
poisons, the body can react to them with other mechanisms that dampen their effects.
Major scientific and technological breakthroughs for drug discovery have occurred in the
past several decades, but the number of new drugs is declining nonetheless. For example, the
human genome was fully sequenced in 2000, and the expectation was that since the genes that
create proteins involved in diseases could be identified readily, then drugs that regulate those
proteins can be quickly found. However, there is no one to one correspondence from genes to
proteins to disease. According to Goozner (2005), the genome contains 30,000 genes but these
6
produce more than 300,000 proteins, and different genes make different proteins at different
stages in life. The ability to genetically engineer single proteins has produced several important
drugs (human insulin, EPO, factor VIII), but very few diseases are caused by a single missing
protein. Diseases such as heart disease, cancer, stroke, Alzheimer’s, or arthritis, according to
Goozner (2005), are not genetically determined in any simple way, and may involve multiple
genes. As well, even when a gene involved in a disease is identified (as is the case with cystic
fibrosis) creating the protein and getting it to the right site in the body is very challenging. The
second scientist quoted above explained that the human genome generates vast amounts of data,
but only some of it makes sense. As well, individual genetic make-ups differ, affecting whether
or not and how drugs work in their bodies.
While the human genome remains an enigma, one might think that the ability to screen
hundreds of thousands of compounds in just a few days and to design them by computer would
produce more drugs, but it has not. According to a Deloitte white paper, the director of R&D
strategy at Eli Lilly explained problems with these major technologies:
…Seven to eight years ago drug companies were enthralled with the possibilities of two
specific technologies, heavily promoted by the biotech community and the venture capital
industry, namely: Combinatorial Chemistry and High Throughput Screening (HTPS).
Both approaches involve an industrialization of laboratory processes to allow large
volumes of experiments to accelerate the search for candidate medicines. But mass
production research carries a price… According to Mr. Schacht, “both approaches did
not always increase intellectual know-how and were of limited value to the drug
discovery process” (p. 14).
A director of pre-clinical development at another firm that we interviewed explained that of the
several thousand “hits” in a screening, most are either “horribly toxic” or not water soluble (and
thus cannot be taken orally and may not be bio-available). This director’s explanation for why
7
productivity is down is that all these technologies have had a “disruptive” effect on this highly
interdependent system, and how to fit them into this system remains to be figured out.
Finally, the existence of productivity problems in drug discovery may come as a surprise
to innovation and learning academics, since so many studies in these fields rely on biotechnology
and drug patents and alliances to indicate that learning takes place (Powell, Koput, and Smith
Doerr 1996).
It is true that the majority of new drugs launched in 2004 were discovered by
biotech firms (Economist June 4 2005), although this fact reflects less of an increase in biotech
productivity overall, but one can argue that this reflects the big decline in “big pharma”
productivity. However, the relatively better performance of the biotech sector might indicate
better management of scientific learning, since these firms focus more on the science than on risk
management and marketing that some say managers in big pharma focus on (Goozner 2005).
However, despite its productivity in patent generation, the biotechnology sector has not been
productive in the profit generation arena.
The sector has lost money every year since its
inception more than 25 years ago, even when the large profits of firms like Amgen and
Genentech are counted. Ernst and Young predicted in 2004 that the industry will finally show a
profit in 2008, but that remains to be seen.
The biotechnology industry has generated
innumerable additional technologies for screening, genetic mapping, measuring, assaying, and so
on, all of which have poured into the drug discovery process. Perhaps the positive effects of all
these enhancements on drug discovery will become evident eventually, as will those of the major
scientific and technological breakthroughs. By examining the underlying processes of learning
in drug discovery and how they may be managed more effectively, we hope to add some
understanding to whether and how these improvements might appear in the industry outputs.
8
Learning In Engineering Vs. Sciences
This brief sketch of the drug discovery activities and innovations indicates that learning
most likely plays a vital role, as it does in engineering. Managers and academics alike have
advanced the efficacy of engineering by understanding the underlying processes of learning well
enough to explicitly enhance them. Procedures such as technology roadmapping, platform and
portfolio management, phase review procedures for new product development, and technologymarketing-manufacturing linking tools enhance performance by enhancing the learning. The
pharmaceutical industry seems to be applying many of these techniques to their innovation
process as well, but these techniques are based on the particular processes of engineering
learning. This learning presumes a “path” along which knowledge can readily accumulate
around a dominant design, core operating principle, or architecture. These techniques may need
to adapted – if not fully reconfigured – to fit the processes of science learning.
To begin to disentangle the processes of science learning, we integrate the considerable
research on engineering and (mostly academic) science into distinct if very general learning
processes. Our intent is not to oversimplify but to bring out underlying dynamics in learning that
may be very different. These differences frame our grounded theory building by providing a
template against which we can compare descriptions of drug discovery work, and help us surface
core properties that distinguish the learning in this work.
Method of Inquiry: In engineering, the method of inquiry is to search for causes to
specific, identified problems through trial and error “learning by doing.”
Problems in
engineering are usually stated specifically enough to be measured (e.g., degrees of flexibility,
deviation from ideal functioning, cycle time of the process, amount of energy that can be used).
These measures directly reflect the end product, and provide a frame of reference that better
9
defines the problem. Once the problem is specified, engineers can identify solution alternatives,
and while they may not know which one will work at the start, they can compare the
performance of alternatives against the measurable criteria, identify the causes of the particular
problems, and choose how to proceed. The inquiry therefore seeks to find the path to a solution,
and can often rely on models and algorithms to find the path, since the central forces and/or
factors are understood (Allan 1977; Rosenberg 1982; Clark 1985; Ulrich 1995).
The method of inquiry in academic life sciences is “blind variation,” according to KnorrCetina (1999). Microbiologists in her ethnography did not try to understand the numerous
problems that arose in their experiments because “… their attempts to understand a living
organism, of which little is known, quickly reaches its limits” (1999:93), and because there are
many ways in which things “do not work” in microbiology, the most typical being that results of
experiments are ambiguous and not easily interpreted. Instead, “(i)n the practice of molecular
biology…problems are treated by varying components of the experimental strategy until things
work out, not by launching an investigation of the cause of the problem” (1999: 92). Indeed,
experiments mostly fail and/or cannot be repeated (Fleck 1979). In his study of corporate
scientists, Bhardwaj (2005) finds similar results: scientists would select a domain for study, and
then select a search anchor around which to search more specifically.
Strong results and
unambiguous conclusions were “few and far between.” These studies all indicate that lifesciences have no path around which knowledge readily accumulates, that causes of problems
remain unknown, and that there are no precise measures or models that can provide direction.
Method of Integration: The method of integration in engineering is brokering, or pulling
together various existing solutions into a new package – ‘putting old things in new combinations
and new things in old combinations’ (Hargadon & Sutton, 1997: 8). Brokering pulls the existing
10
pieces together into a physical product that represents the “integrity” of technology and user
needs, and of the firm’s technologies (Clark and Fujimoto 1991). This pulling together is
enabled, indeed framed, by the creation of holistic templates or systems such as a product
concept (Bacon et al, 1994), product platform (Meyer and Leynerd 1997), and an “application
context” that reflects the whole design and manufacturing system (Iansiti 1998).
These
templates represent the product and how fits into the application context of users and of the
manufacturing process, and provide engineers and colleagues in other functional areas with a
common vision of what each is to do, why, and how (Leonard 1998; Dougherty 2001). Complex
engineering integrations can be further enabled by modularity, which allow systems to be disintegrated into discrete components that integrate with a standardized interface (products in
telecommunications, games, software). Modularity reduces the intensity and complexity of task
coordination, and makes it easier to incorporate new technologies (Baldwin and Clark 2000).
The method of integration in life sciences has not been explicitly described, since most of
research that examines underlying processes focuses on academic science, where integration
occurs at the institutional level (the paradigm, peer review). Academics are not concerned with
integration, but rather with publishing ideas.
Knorr Cetina (1999) finds that work in
microbiology is achieved in fragments, and that the objects of study are detached from their
natural environment and transformed so they can be studied in the lab – just the opposite of
creating wholes. Similarly, Kline (1987) argues that because science often abstracts phenomena
to a few essential properties, it is difficult to apply science directly to the complex forms of real
products (c.f., Floricel and Dougherty 2006). Fragmented findings and non-modular systems
may make integration very difficult in science. But research does establish that integration
enhances drug discovery (Henderson and Cockburn 1994), so we know it is important.
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Method of Sensemaking: Sensemaking in engineering is based on understanding factors
that are governed by physical laws; practitioners typically know how and why the systems they
work with function. This sensemaking is hands-on doing (Rosenberg, 1982); it is concrete and
physical since products can be seen and touched. As Weick, Stucliffe, and Obsfeld (2005: 412)
explain, “to make sense is to connect the abstract with the concrete.” In engineering the abstract
readily connects with the concrete; the abstract suggests action.
The method of sensemaking in life sciences is best described as the “sense of the
scientist,” according to Knorr-Cetina (1999) who examines this sense in the context of academe.
While similar to the hands-on, skilled practice of engineering, the sense of the scientist involves
all the senses and is both cerebral and unconscious. Materials need to be visually inspected but
holistically in their setting. “Many scientists feel it is impossible to try and reason through the
problem or to pick up the important clues from oral or written descriptions. In order to know
what to think, one has to place oneself in the situation” (Knorr-Cetina,1999: 97-98). Results are
not readily translated from one scientist to another: “Results that are not seen directly or not
produced through embodied action cannot be properly evaluated and are prone to
misinterpretation” (Knorr-Cetina, 1999: 98). The logic connecting the abstract to the concrete is
not linear, and it is not readily understood. Scientists act like ensembles of sense and memory
organs and manipulation routines onto which intelligence has been inscribed. Sensemaking is
described by Knorr-Cetina (1999: 109) as:
“The experienced body of the scientist, when it operates, naturally brings its experience
to bear on the variation it concocts for selection by success. The retries scientists perform
are never just any odd random alterations. Instead, they are based on what a scientist
‘senses’ to be a promising strategy in a problem case.”
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The sense of the scientist is inextricably linked and embedded in the activities that the scientist is
involved in such that each constitutes the other (Weick et al., 2005). Scientists are constantly
faced with the unknown and are therefore constantly inventing new meaning based on mental
models situated in their work context.
The contrast between engineering and science suggest three significant challenges for
managing learning in drug discovery that we will explore. First, how does science-based work
proceed when there is no clear path for it to follow, no determinable cause for problems, and no
model to test things/no accurate criteria against which to test things?
Second, how can
knowledge be integrated when there is not an understandable whole that it can be integrated
into?
Third, how do scientists, faced with deadlines and budgets, make enough sense to find
drug candidates, when the work is fragmented, and there is no path to follow, no clear
understanding of why problems occur, and no model to provide direction?
METHODS
We used grounded theory building to begin to build new theory about the processes of
learning in the pharmaceuticals industry. Qualitative methods, specifically, grounded theory
building, is uniquely suited to probe the complexity of this phenomenon and to capture the sense
of utter ambiguity that pervades the innovation process in this industry.
Data Gathering:
We gathered data by interviewing people who work in the drug
discovery and development process in seven large pharmaceutical companies. The interview
subjects were involved in either discovering or developing new drugs or the processes that
enable the discovery and development of new drugs. Both authors carried out the 57 interviews.
Since drug discovery is an extensive and complicated process, we tried to interview people from
different aspects of the process.
Interviewees included chemists, biologists, biophysicists,
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biochemists, pharmacologists, bio-informatic experts, formulation experts, manufacturing scaleup experts, preclinical and early clinical experts, business development managers (who oversee
alliances and licensing), and business planners. People in the biotechnology firms were either
engaged in drug discovery themselves which involved “big pharma” or worked on technologies
that they would try and sell to big pharma. By level interviewees include several heads of R&D
overall, scientific directors of discovery by therapy area and by discipline, project team leaders,
and bench scientists. When people who manage others were interviewed they were asked to
identify people for future interviews.
Each person was asked to describe what they do and how they are involved in the drug
discovery process. These were semi-structured interviews (Strauss & Corbin, 1998) that allowed
each subject to tell his or her story from their perspective in the process. We asked people for as
many concrete examples as was possible, in order to reduce memory bias and to aid our
understanding of what they were describing. We used a ‘cheat sheet’ of biological and chemical
terms to insure that we understood the issues brought up by the discussion. Although many
issues were scientifically related they were somewhat consistent across the interviews so we
were able to build up a stock of chemistry and biology knowledge that enabled us to follow the
subjects. The interviews lasted approximately an hour and were done at the subject’s work site.
Our focus is on the process as a whole, not a specific project, since we are interested in
how learning works in the general drug discovery process. Most people discussed their work,
their view of the overall process, and how what they do connects to the rest of the organization.
Most discussed the challenges and the uncertainty of their work, sometimes comparing it to work
in other industries. These interview data reveal what subjects chose to reveal, and we were clear
that we were not interested in any confidential information, simply a person’s perspective on
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their work and experiences. We entered these interviews with the assumption that learning was
important for pharmaceutical companies, but not that they were learning. However, when people
were asked about learning they were unable to provide examples. Instead we asked for details
about their work, what they were able to get from their various projects, and what they wished
they could do differently.
We actively searched out and spoke to people in different pharmaceutical firms. All of
the companies are confidential per our agreements; however, we can reveal that each is a well
known large pharmaceutical company. Because the companies are similar in size and in the
same industry, technology and market dynamics can be considered roughly equivalent. The data
collection and analysis process was carried out over nine months; this process continued until
each of the categories was well established, and all of the variations among the key categories
had been thoroughly investigated. This is a process called theoretical sampling, as defined by
Strauss and Corbin (1998). However, as we noted in the introduction, this is a preliminary report
of our research; because of this we are still collecting and analyzing data.
Although organizational and perspective differences were apparent in the data, all of the
subjects were working with the same complex process. In order to provide a contrast with our
data we compared how the work is done in this industry to how existing literature describes
learning in engineering industries. Some of this contrast was apparent in our data and we
referred to existing examples in the literature to make this contrast clearer. Because this is the
first time that the learning processes have been explored in this industry at this depth this study
simply attempts to uncover the learning processes and expose their significant differences with
accepted theory. We found similar learning processes across the collection of organizations,
which suggests these results are generalizable at least to this industry.
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Data Analysis Leading to the Grounded Theory: Grounded theory building is inherently
subjective since researchers are interpreting people’s experiences, so the analysis requires
disciplined questioning of ideas across researchers and events in the data (Bailyn 1977), to
continually check alternate possibilities.
We followed the specific discipline described by
Strauss (1987; Strauss & Corbin 1998), and elaborated by Dougherty (2002): “open coding” (to
surface many possible categories), “axial coding” (to hone categories and articulate properties), and
“selective coding” (to articulate a core category that integrates others into a theory). Coding is
defined as the “... analytical process through which data are fractured, conceptualized, and
integrated to form a theory” (Strauss & Corbin 1998:3). The coding process is an interactive
process that includes shuffling between the data and existing literature in the field in order to
build more robust categories.
The two researchers met twice a week over a roughly three month period in order to
discuss the interviews and code ideas about the overall process of drug discovery and
development and the learning in that process. After each meeting we wrote up analytical memos
and circulated them. We also coded our interviews independently to insure that we were not
biasing the other’s interpretation of the data. Once certain categories (learning processes or
characteristics of learning processes) we looked across the organizations to insure that what we
were finding was not organization specific. One of first things that struck us was that people
were not concerned with learning – they seemed to focus on concrete results rather than on the
processes by which they got them. As we analyzed the data, looking across organizations, it
became apparent that there was no clear accumulation of knowledge from project to project.
While individuals accumulated experience which was useful, there was not a systematic way to
apply past experiences to new projects other than having the scientist who worked on the past
16
project involved in the new one. In an industry where knowledge is central this seemed very
surprising. In addition, although companies spent large amounts of money on information
technology to help store and analyze the data, people working in this area were constantly trying
to ‘get into the heads’ of scientists so they could provide them with better information, because
they didn’t know the best way to use all the technology. The theory presented in the next section
began to emerge as we saw similar concerns and processes across the organizations; and when it
was clear that these processes contrasted with similar processes in engineering based industries.
One limitation of using semi-structured interviews is that we rely on the subject’s
interpretation of the situation, his or her story of what happened, compared to ethnographic data
where the researcher observes what is happening. However, ethnographic studies are typically
limited to one setting, and by using interviews we were able to collect data on multiple
organizational settings and to insure that the processes we identified fit more than one setting.
FINDINGS
Summary Overview
Our analysis suggests three approaches for inquiry, integration, and sensemaking in drug
discovery that are much more like the learning approaches in science, but they apply scientific
learning to product development, and thus are much more socially interactive than the learning
approaches in academic science. First, “blind variation” becomes a process of searching for
clues in drug discovery. The searching goes on in a complex, multi-dimensional space of
possibilities for drug candidates, during which scientists work like detectives, not engineers,
noticing, gathering, and really digging for clues. This variation is blind to causes as is academic
life science, and is pushed by scientific inquiry rather than pulled by a given path. This learning
process is also shaped by intermediary criteria that roughly reflect the “goodness” of possible
17
targets and molecules.
Second, we find that drug discovery requires extensive integration since the process
involves many specialties, each with its own clues. Integration occurs through iterating and reiterating among specialties to combine clues into patterns. Rather than brokering, iterating helps
to select among clues and “round them up” into “bodies of data and evidence” around a
hypothesis that a given molecule will solve a medical problem. The iterating results in choices
for next steps of clue searching, occurs throughout all activities for discovery and development
(the activities are displayed in Figure 1), and takes place at different levels that are working on
different sets of clues (disease, therapeutic area, pipeline, business franchise). Finally, we find
that the more individual “sense of the scientist” of academic science operates as well, but must
work within an organizing system that fosters navigating the multi-dimensional space. The
organizing system of work groupings, connections, and decision making becomes the sensing
organ, much like Fiol’s (2002) idea that organizational learning is similar to the human brain,
full of neurons that network around and “fire” based on cues and connections.
Our data suggest that these complex learning processes go on “behind the backs” of
people engaged in drug discovery, as do most complex social processes. But the possibility that
people do not pay much attention to them means that people may not deliberately enable or
enhance the learning. Learning in drug discovery can be largely idiosyncratic, because these
three learning processes are easily disrupted and lend themselves to eccentric and fragmented
insights that may not accumulate readily. We also find that technologies associated with drug
discovery may at times be used in an engineering mode, not in a science mode.
Some
technologies are treated as solutions, not as clues, and some are imposed on other data rather
than woven iteratively into a pattern along with other clues. Finally, pharmaceutical managers
18
may, however inadvertently, suppress the learning in their efforts to introduce discipline (be on
time and on budget) and reduce cycles times. These insights suggest three ways to enhance
learning in drug discovery: dealing with the lack of knowledge accumulation that may
accompany these learning processes; avoiding the mis-use of engineering learning; and being
mindful of the need to integrate technology and science within and across levels of analysis.
First, we present a lengthy example of each learning process to show that the three
processes operate not only simultaneously, but in concert. The quote comes from a scientific
director of discovery biology who was asked to describe how the work proceeds and how
expertise is leveraged, and he mentions all three processes in one brief synopsis. First, he
explains the subject itself and makes clear that this work is science, not engineering:
You want to know OK – you have now picked the most likely inhibitor of this enzyme
now I want to understand what it does in a cell – in a hepatocyte – a liver cell or a cancer
cell. One of the set of experiments that I need to be able to do to be able to understand
what this drug does in a cell, then the next question is OK I now understand what it does
in a cell and it does what I want it to do – it lowers the ability of the liver to generate
lipids or sugar or a cancer cell to grow.
He emphasizes the need to understand how the inhibitor works in the cell. He then describes
how they try to understand more about the possible drug, not by searching for causes but by
raising questions and looking for indications, or clues. He outlines the kinds of clues the
scientists need to figure out if this idea for a drug will work in humans:
Now how do we understand how that is going to translate into studies in whole animals
where we have to deal with other issues – is the drug metabolized in a way that it all gets
broken down to something that is no longer effective – or can it deliver the right models –
I want to study in animals that seem to predict for activity in humans, so what kind of
models I need to understand – develop, build, create and the begin to test new compounds
against…
In the third paragraph we begin to see iteration, how the project “touches on a lot of
different expertise;” the project literally gets passed back and forth between different parts of the
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organization, and that process integrates new knowledge into the project.
For chemists it is different vision, for protein biologists it is a very much different vision
but the morphing of a program by and larges goes through – most of the steps go through
kind of biology hands and then touches on a lot of different expertise until it ultimately
gets toward the stages of it becoming a drug that is going to go into people…
Note that he mentions “the morphing of a program” (a “program” is a drug candidate). This is
not a linear process, but rather a cycling back and forth and up and down and around, so the
program moves forward but it also shifts and emerges.
Finally, he describes the collective sensemaking that involves interpretation and
judgement of a variety of clues and their re-iterative shaping:
If they interpret well then they have done the right experiment that proves that we have
met that hurtle – again some of these are technical and some of these are more
philosophical – if you cannot express the protein that – experimental that you knock the
gene out – you knock the gene out and the cell completely changes and reverts to a
normal form of cancer cell – we get real excited – now let’s make the protein that the
gene codes for and study it.
They must interpret “well,” figure out the “right” experiment to generate the right data, and they
get “real excited,” so the choice to go forward from here is not simply rational, it involves
passion too. The sensemaking underlies the first two learning processes since the right question
starts the clue searching, combining the various hurdles integrates clues, and iteration leads to the
development of additional clues which are followed as dictated by the sense-making bodies of
the scientists working together.
A chemistry leader in another firm was asked why organizing by therapeutic area, as they
did, was better than organizing by scientific discipline (e.g., biology then chemistry), and he
explained that so doing enabled what we are calling the collective sense of the scientists:
…sometimes chemists are thought to be interchangeable and sometimes they even sell
themselves that way, but one does develop an expertise over years of experience where
you start to know- you gain insight into a specific surrounding – making a drug to treat
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pain or a drug to avoid say a side effect that is typically associated with analgesic agents
and that is very valuable information and something that is really hard to capture. Part of
it is intuition and part of it is experience – some of it is an eye for how to make the
molecule better but some of those intangibles are uniquely qualified with certain
therapeutic areas.
People have intuition, experience, and an “eye” for how a molecule can be made better. They
get these diverse senses in part from their social experience of working in their respective
therapeutic areas.
While remaining cognizant of the way these three learning processes work together, in
the following descriptions we disentangle them in order to explicate them clearly. After we have
explained them individually, we return to a more holistic view and suggest how the details of
each process can be leveraged to improve accumulation, avoid treating the process simply as one
of technology management, and leverage the synergies of so many technologies and sciences,
since this industry, at least, does not have the luxury of separating them.
Details of the Learning Processes
Searching for Clues: The method of inquiry in drug discovery is searching for clues,
not for causes, because, as in academic science, causes cannot be easily discovered and the
problems are not fully or precisely specified. The work proceeds with no clear path and is driven
by a scientific step-wise search for clues to understand a target’s role in disease and what binds
to it. The problem “find a drug” is a big one that cannot be broken down in to separate steps
since the various activities are highly interdependent. Instead, drug discovery scientists try to
preserve the whole of the problem space and work like detectives, exploring this
multidimensional space by reaching out and trying various experiments, asking good questions,
finding reasonable answers, and moving forward by filling in some of that space. The process
unfolds over time but clues can begin to align, so next steps do build on prior ones. Searching
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for clues is exemplified by this biologist’s account of how she and her team created a new kind
of molecule for a kind of kidney disease (the molecule is now in phase I clinical trials):
We wanted to try and find other molecules like that (pointing to a picture). The natural
ligand is much larger and there are many key connection points on the receptor.
Everyone said you can’t do it… We had some clues that we thought would be important.
There were other receptors in the same family and other clues about what could make a
connection… We visited another pharmaceutical company and different biotech
companies... We used [an alliance partner’s] technology to screen for peptides. This
[pointing to the model on the wall] has no identity at all with the original ligand, it is not
known in nature, we had to create it… I remember very vividly thinking it was a waste
of time. And we were trolling in a couple of other areas like natural products and
antibodies…
She describes collaborations with other firms, consistent with the numerous academic studies
that indicate that such relationships are important in this industry. When we seek to tease out
how learning through alliances actually unfolds, it seems here that different companies have
different clues that may not have much value on their own. Drug discovery learning therefore
requires a purposeful searching for clues that matter. In fact the clues are just that, indicators of
a possible direction without much meaning on their own. This scientist and her team were trying
to zigzag among these clues, driven by their objective of trying to create this different kind of
molecule (details are deleted to protect identities). The biologist describes many steps going off
in a variety of directions to search for clues: “other receptors in the same family” and “other
clues” about connections; insights from other companies who had special screening techniques;
and “trolling in other (but related) areas.”
This illustration highlights two key aspects of searching for clues: 1) it is a stepwise
process, often one step in one direction and another step in another direction, but things can
begin to point to a possible path so it is not just shots in the dark, and 2) it is goal-driven: find
clues that indicate whether or not a drug does or can work, although this goal is very tenuous and
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so requires leaps of faith. Both the steps and the goal are shaped by intermediate criteria such as
some indication of action against the protein, toxicity (is it cancerous, does it react with other
targets, and, especially since the Vioxx troubles, does this molecule have any cardiovascular
interactions that we do not like); solubility; and absorption, distribution, metabolism and
excretion (known as ADME). These characteristics are like signposts in the clue searching,
because clues that indicate the presence or absence of these markers suggest to the scientists how
a potential drug will behave in the human body.
These intermediate criteria are vital in the work because of the protracted timeline that
scientists must work with. A potential new drug is not tested in humans until it has been under
development for several years, and the only way to truly figure out if a drug works is by testing it
in humans. Thus, along the way, the results of these tests are just clues, tenuous ones at that.
Even during the clinical trials the clues are indirect and require considerable interpretation, as
this chemist explained:
You can analyze the signaling of the this receptor in this piece of skin and you can show
that the drug is affecting that signaling – that is a XX marker, it is not a tumor but you
cans show that the drug is actually inhibiting signaling in a tissue that expresses that
receptor.
This clue from the marker shows that “the drug is affecting that signaling” at least in the skin that
also expresses this receptor. Clues do not provide a finite answer, but instead reach out to
suggest a number of possibilities. For example:
This is a proxy measure – what does that have to do with human depression…the
connection to the disease is so often somewhat tenuous but there may be this correlation
of effects and you are using that correlation and that model to at least say ‘did they get
the drug to the appropriate place to mediate an effect that I can measure’ does my drug
get there and does my drug do the job then you know…
The tenuous nature of searching for clues means that drug discovery is punctuated by
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leaps of faith regarding whether or not a possible drug will work. As this biology team leader
explained, discovery scientists want to know:
Does the molecule behave the way that you would like it to or think that it should in your
best in vivo model. So when we see that activity we get very excited and predicate our
advancement of the project on that basis to a great extent.
They must leap from experimental clues to judgments based on “the way you would like it to”
behave, or “think that it should.”
To summarize, searching for clues is an inherently exploratory process that is based on
science – people are trying to understand how a portion of a biological system works, although
they try to apply that understanding right away to drug discovery. Searching for clues raises the
possibility that knowledge and learning will not accumulate readily because there is no
established process or routine, and because the processes that are followed to find one clue may
fade quickly as the search goes on to more clues. Just focusing on this learning process raises
certain issues for management. For example, how well is the process of searching for clues itself
enabled, and what might push it inappropriately into exploitation? How well do the numerous
technologies enable the search for clues, and are some used instead as “clueless” solutions? And
can the problem space itself be framed differently to enhance the search for clues? As our
examples illustrate, people focus on specific targets and molecules rather than on the disease or
the therapy areas, but it is also possible that these larger systems introduce too much ambiguity.
The search for clues is just one part of learning and is complemented by integration and
sensemaking, so a more complete understanding of the learning challenges in drug discovery
requires some analysis of these processes as well. We turn next to integration by iteration.
Integration of clues through Iterating: We find that integration is essential for product
innovation in the pharmaceutical industry as it is in most other industries. Scientists need to put
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together a body of data to support their core hypothesis that their drug works, and this “package”
must “… engage the rest of the matrixed organization to get behind your idea and develop a
drug” as one science director put it. Indeed, a package must be put together eventually that
satisfies the FDA and other regulatory bodies as well. However, the method of integration must
overcome the fragmented nature of biology work, and the lack of holistic frames like
architectures that define how parts go together. We find that people engaged in drug discovery
rely on iteration among specialties to create temporary and partial wholes at all levels. The
iterations cycle around different specialties to “round up” clues into a pattern of what is going
on.
This continued rounding up and synthesizing makes some clues more sensible and
meaningful, and eliminates others from further consideration.
Iteration is based on
interdisciplinary collaboration, since the insights of one science or function are deliberately
juxtaposed with others to see how all these different insights fit into a pattern. This iterative
collaboration requires concrete, situated work with the biological system, goes on at all levels
although it is not clear that the various levels are explicitly managed as iterations, and it
facilitates decision-making.
This chemistry team leader describes iterative learning between the chemists and
biologists in his therapy team:
… we can all make a prediction as to what kind of potency we think we need but it is a
reiterative process where the biologists will not only provide the data but can tell us a lot
about our molecules that we could not foresee such as – how do they look when they
dissolve – do they dissolve – how did the cells look after they saw the molecule…It is
reiterative – it demands a lot of creativity and it is a very competitive area so we have to
work well together and again try to develop molecular chemistry…
Iteration creates new clues and new insights from existing clues, since the biologists “tell us a lot
about our molecules that we could not foresee…”
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This same scientist emphasized the concrete nature of their iterative work as they push
along the clue searching from simple to more complex models of the disease biology:
(early molecules) will be screened in an in-vitro setting against activity within the cell or
it could be cell membrane or an enzyme or what ever and we will get a quick read out on
that – that kind of screening might take a day or two and then from there we pick our best
molecules .. and we try to build upon that activity and as we improve the potency of the
molecules, they go into tissue base and then eventually rodent based models and so it is
reiterative process that really drives the science forward. …You try to get potency there
[in a cell mechanism] because that is a little more complex of a system. So as you go to
animals you hit different roadblocks or challenges so you go back and try to redesign
your molecule and try to figure out why the molecule is not doing precisely what you
wanted to do – does it have good bio-availably in a body – it is really potent or maybe …
it is also toxic because it is hitting some other targets that you don’t know.
Iteration drives the science forward, he says, and they go from in-vitro cells to tissues to animals,
and back as they “hit different roadblocks.”
We understand this learning process of iteration as the process of integrating into a whole
when there is not a whole to integrate into. Instead we have partially closed spaces in the larger
multi-dimensional problem space that is being searched, and these partial wholes are created
over time as people go forward with clues, reach across to other units and insights, and reach
“up” to the disease area strategy or the management system, and then reach back again as
roadblocks are encountered. A scientist in a small biotech firm described the integration process
as a “spiral” rather than an endless loop because there is always movement. The iteration
process facilitates decisions by bringing in new information and by bringing in better,
increasingly narrow information together around key decisions.
Iteration also helps people focus in on useful clues. A scientist, speaking of the high
throughput screening outputs, said: “we would try to eliminate the ones that we thought were
say molecules or classes that we didn’t think we could make easily.” The information for this
focusing and honing comes from interacting with different groups that provide data on issues
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such as can we make this easily, is the protein itself available or make-able, have we seen this
before in toxicology screens, and so on. In fact, people pointed out that they have learned to
recognize the iterative nature of their work, and to fold the output of HTS into their iterative
process, as this chemistry VP explained:
One of the changes over the last 15 years or more… what happened is that we went to a
time when we thought that everything would be high throughout screening. We thought
that by sheer numbers a gem would pop out. No, that didn’t happen. Now we are
smarter. It is an iterative process. First you get the data and then redesign the chemistry
and rethink it, so we have shied away from that HTS model. (I asked about iterative).
More iterative, this is still a pretty empirical field. You cannot predict the outcomes
efficiently. We still have to put the molecule into an animal model and at the end of the
day into a human. We must observe, so we cannot bank on an initial set of data. There is
no crystal ball. And you can’t run all through a half a million compounds in all tests.
We also see different levels of iteration in the data that may embrace different time scales
and different scopes of activity.
For example, as the clues about compounds are coming
together, the discovery teams will propose to move them forward into the final stage of discovery
overall, pre-clinical testing. Typically another team of experts is formed around each potential
compound to include clinical trials experts, and they integrate the clues into other packages, as
this director of preclinical explained:
…the discovery people would start working on certain targets but sometimes they would
work with animal models of a variety of different things so they might test it [a
compound] out in six different things that approximate human diseases – nothing is
perfect – they might do something that resembles asthma, rheumatoid arthritis and even
multiple sclerosis with the idea that it would probably go in one of those directions but it
really then takes a full project team or project development team to get everybody’s input
– some of it like the commercial input has to do a little bit with what else do we have in
our product portfolio and how does it fit in – and the clinical input in terms of something
like multiple sclerosis is an important area to study but you don’t know you have true
proof of concept and an effective drug until you are virtually through eight years of phase
III testing and raises different kinds of issues. Basically it is an interdisciplinary team that
works out the full plan going forward as well…
Integration in drug discovery is an inherently interdisciplinary process of rounding up and
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weaving together clues about the possibilities of a drug candidate. Iteration underlies all key
decision points, and in fact turns those decisions into processes. Iteration occurs at different
levels which take in different scales of time and different scopes of activities. Our data suggest
that these different round-ups of clues may not be explicitly managed as such, however, since
most people at all levels seem to focus on “the compound.” The iteration by different levels
raises several more questions that can be explored for learning. For example, can knowledge
accumulation be managed more explicitly by paying careful attention to the different levels of
issues such as target and pathway, disease, side-effects? What exactly is being accumulated in
these different cycles of iteration, and how and who well? As well, there seems to be a key
boundary between technologies and the sciences which may need more explicit management,
since the knowledge on either side does not translate readily and may require bio-informatic
boundary objects that are designed expressly to enable each side to recreate its understandings
(cf. Carlile 2004). There also seems to be a knowledge boundary between “the business” and the
science. While our data regarding this point are still preliminary, we do find that each side is
“pushing” its issues and point of view onto the other. Finally, all the examples so far indicate
that enormous amounts of complex sensemaking takes place all across the discovery process, so
learning management issues must incorporate this process as well.
Making Sense of It All with Collective Navigating: Finally, we indeed find that the
“sense of the scientist” described by Knorr-Cetina (1999) is the primary vehicle for making sense
of all these clues and partial patterns. The sense of the scientist operating in pharmaceutical
organizations as opposed to the academic labs that Knorr-Cetina (1999) describes must contend
with the organizational imperative to create new drugs. The sense of the scientist is very
complicated; it incorporates the whole body of the scientists, unconsciously cerebral, and fully
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situated, engaged in a system of people. People who must make sense together, since they are
engaged in the highly interdependent and extremely ambiguous innovation process of creating
new drugs, on time and on budget. We choose to label this process collective navigating based
on the comments of one research director who described the work as “a twelve dimensional
space to navigate in.” The dimensions refer to the many properties that any compound must meet
such as efficacy, toxicity, solubility, and so on. He later increased the number of dimensions,
saying that this is an eighteen dimensional space, with the bar going up all the time with new
regulations and new expectations. Navigating is an organizational challenge since no one person
can not do it alone.
The sense of the scientist is easiest to see when it is described at the individual level, but
the individual sense constitutes and is constituted by the collective one. The sense can be seen in
the previously quoted biologist searching for clues:
Science is about knowing when to stay the course and when to leave. (how do you
know). You know the data to access and the experiments that have to be done and
whether or not they will cover enough (of the info or situation?) to answer the questions
and test the hypotheses. And your experience with how long it takes to break through a
technical barrier, and whether or not it is worth your while to stay with it or go over to
something else.
Sensemaking is about “knowing;” it how a scientist knows things. This knowing is remarkable
given the ambiguity that operates in. As one scientist explains, even if “you know in the body
exactly how [a drug] will work…what you don’t know is the body’s other mechanisms which
will be kicked in to compensate for something that you have dampened; that is the big
unknown”. Scientists are constantly dealing with the unknown, like this, but must decide what to
do and how to move forward nonetheless.
The scientists do not have the language to describe this process of sensemaking, and even
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at this point, we are just beginning to articulate this sensemaking process. For example, one
scientist explains it as “risk, luck, intuition;” he lists of numerous ideas for why an idea worked
because he can not accurately articulate the connection between interpretation and action. Other
scientists describe it as “something that is really hard to capture.” Because the sensemaking is so
hard to articulate people tend to call it luck, but luck is not an accurate description because luck
implies knowing without the use of a rational process. Scientists can not articulate how they
know things but they do say that they know things. We suggest that one way to describe this
process of sensmaking is by depicting it as navigating, sojourning, through unknown territory.
These scientists are navigators who recognize clues in the flow of every day activity that
includes recognizing, interpreting, and acting. As Weick et al. (2005) describe this navigational
sensemaking is guided by mental models of work, training, and other experience.
The
navigational sensemaking is situated in the systemic setting of the organization.
The organization must be designed and managed so that all these people can navigate the
multidimensional space heedfully or with collective intelligence (Weick and Roberts 1993;
Dougherty and Takacs 2004) – that is, with regard to the collective task of drug discovery and
how individual activities inform and fit into that collective task. The largest category of changes
we find in the interviews concerns transforming how the discovery work is organized, bundling
up different steps to provide collective work but also separating activities to break things down
and reduce complexities. One director said that:
You create…teams of scientists that are working at various phases of the discovery
process, in other words there is a team working on discovering genes of interests, you
have a team that is working on validating those genes, you have a team that is working on
converting those genes into an understanding of a protein at a level that enables us to
begin to approach it from a drug discovery process where we could begin to design drugs
against that target. Then you have a team that gets involved with crafting that drug that
modulates that target and then it ultimately goes out to clinical development. The
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difference being compared to academia – each of those tasks in academia would be an
independent investigator dealing with each of those issues. By forming teams what you
are doing is one creating synergies around the expertise that is required as well as
allowing for a more robust triage process.
He describes four separate aspects of the task: “discovering genes,” “validating those genes,”
“converting those genes,” and “crafting that drug.”
Each of these four aspects requires
complicated navigation. Then, “by forming teams” they work and create synergies around their
specific expertise. Organizing is what enables heedfulness so that all the people can navigate the
space mindful of the overall task. In another example, a company that had emphasized one
scientific discipline in the process is breaking out of its “science silos” and organizing around
disease groups, this gives the groups responsibility for going from the early stages of discovery
into the clinical trials process. Both of these are examples of heedful organizing; allowing the
groups to navigate independently and together.
Navigating in the sciences means moving through the multi-dimensional space of the
actual work of science and working with regard to the collective task. As we have explained
navigating requires a scientist’s whole sensory self, not just the physical. This sensory self is
situated in everyday action in an organization and must be recognized as such.
IMPLICATIONS/DISCUSSION
One major lesson learned in 20th century industrial management, albeit engineeringbased, is to pay careful attention to the processes through which people figure out what to do,
how to do it, and how to get better at it – i.e., learn. This study is just a first effort, and a
preliminary one at that, to explore the basic processes of learning and knowing in the enormously
complicated drug discovery process. But we can draw a few initial conclusions and make
several suggestions for how to study the learning processes in drug discovery, and perhaps in
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science-based industries more generally. First, we hope that our brief overview of the subject
matter of human disease and how people in this industry are trying to address it has given some
glimpses of the utter complexity and nearly infinite profusion of “datums” that are involved in
human diseases and their remediation. Even if databases could be constructed to store and
instantly retrieve all the information that have been and are continually generated in drug
discovery, enhanced human learning and sensemaking would be absolutely essential. Moreover,
there is an endless need for assays, models, and other techniques, so just generating these
databases would not be enough.
The solution is not technological despite calls for better
techniques (Grove 2005), although obviously technologies will help provided they are developed
and used to facilitate the inherently science-based learning. The solution to this problem is about
enabling and enhancing the human learning that is involved in drug discovery and development.
Second, we find that the processes of learning in drug discovery differ significantly from
the more familiar engineering processes, and cannot be managed in the same way – no PERT or
GANTT charts are possible, and conventional project and total quality management programs
may not apply either. But by understanding what the actual, science-based processes of learning
are like, we can develop ideas for how these learning processes can be leveraged in their own
right to improve drug discovery. We develop implications for managing the special challenges
of knowledge accumulation that science-based learning produces, for weaving engineering
solutions into the process more appropriately, and for organizing around three major knowledge
boundaries so that they can be traversed more effectively – between science and technology,
between science and business, and between all three and the human body. At the very least,
these three kinds of processes should be fully examined.
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Searching for clues, iterating among specialties around possible drugs, and relying on the
virtually mysterious, collective sense of scientists all lend themselves to steady knowledge
depreciation rather than accumulation (Argote 1999), since processes and procedures fade
quickly, intermediate ideas dissipate when a drug possibility is found, and attention focuses on
those drugs individually. What people learn does not accumulate readily in routines, machinery,
or even products since the drugs are not systems. First, our findings about searching for clues
and iterating to round them up suggest that the domain of search might be better managed and
framed so that what is learned can be tracked. People in all companies are actively trying to find
better domains of search by adjusting the basis of organizing – by disease, therapeutic area, or
pathway, for example, moving away from pure disciplinary search domains such as chemistry
versus biology. One director, a microbiologist himself, said that if it were up to him he would
get rid of the microbiologists (who focus on abstracted cellular mechanisms) and bring back
pharmacologists and physiologists, because these “old fashioned” disciplines are more based on
how diseases and the body work. There may never be simple paths and relatively simple arrays
of machinery in the life sciences that invoke clues, but we do see the need for realistic and
concrete grounding of the search in actual bodily processes and systems. Different companies
are experimenting with different ways to ground the work in rich, systematic domains that reflect
human biology or at least a disease. Thus, what defines a domain for search remains unclear, but
deliberate efforts to step back and think this issue through are vital.
Second, knowledge accumulation can be enhanced if the processes of searching for clues
can be articulated and improved. We have shown that being engaged in the actual search process
is important to scientific learning and sensemaking, so these processes cannot be pulled out and
mechanized or “industrialized” in separate shops, as has been the tendency in this industry. But
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these technologies can be used if they are explicitly managed to enable the search, iterating, and
sensemaking processes. We find that directors of enabling technologies and genomics are
actively trying to support rather than replace the science-based learning. Although much work
needs to be done since this may be one of the most challenging IT management problems yet.
Indeed, companies are creating their own systems, with various numerous external collaborations
and using their own “home grown” experts since products do not exist. One director of bioinformatics claimed that his group was on the leading edge of academic computing and multi
media (publishing in the top journals). Their goal, he said, was: “…to present that information,
organize it properly, categorize properly and present it to our users, our scientists in a way that
would allow them to make decisions – what to do next and what molecules to design, what
compounds to advance, etcetera.” A genomics director at another company said: “You want to
get the essential nugget of information to the forefront from experiments in more facile ways,
and that is through data processing and all that.” Part of enhancing the process of clue searching
is to develop better clues, and better intermediary signposts around which knowledge may
accumulate better. The enhanced markers perhaps can be boundary objects that mediate the
disparate thought worlds of technology and science, but if so and how must be explored.
Third, knowledge might accumulate more fully if people explicitly managed the clues
themselves and their iterative integrations, not just the outcomes of the clue gathering and
integration. This implication is more tenuous because we require more data and analyses to sort
it out. But we were struck by the inference that everyone is managing individual molecules,
which people said were unique and idiosyncratic – thus non-scalable. It seems that several
distinct systems of knowing and doing operate in drug discovery, and they can perhaps be
explicitly managed in their own right. Some people could manage the disease or therapy areas
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to try and accumulate scientific learning more systematically, and develop business and
competitive strategies at this level of analysis. However, the very sparse knowledge may make it
impossible for individual companies, however large they are, to accumulate much knowledge at
the disease or therapy levels – a question for future research. We do feel that the overall process
of drug discovery can be explicitly examined and managed in its own right, and clues can be
iteratively accumulated to improve the process and the decisions being made. New technologies
can be evaluated for how well they support searching for clues and sensemaking, and for how
well they can be woven into the iterative processes of rounding up clues. Questions like how can
business and strategic management do a better job of intersecting with the scientific learning and
leveraging the science should be raised as well.
One last frontier for further study concerns the collective “sense of the scientists” process
for making sense of all the clues and their partial integrations. This aspect of our findings also
requires much better grounded theory than we have provided thus far, and is a major focus of our
continued research. We find a strong individual/organizational duality here. One the one hand,
it takes a highly skilled, talented, and experienced scientist to “get” the implications of all these
clues and that cannot be ignored. But on the other hand, there is so much to understand from so
many different disciplines and specialties that the whole organization and indeed the whole
network that includes the hundreds of alliance partners working together at any one time must
participate in the sensemaking. This organizing challenge is much more complicated than we
have seen anywhere else, since so many more specialties are involved – these include all the
“usual suspects” such as manufacturing, marketing, and sales, all the clinical trials specialties
(MDs, statisticians, etc.), and all the scientists and technologists outlined in figure 1 and the
Appendix. As we have mentioned, all the big companies have reorganized their discovery and
35
development processes thoroughly to try and capture this intelligence more fully, but the jury is
still out on effectiveness. One director of pre-clinical development said that he suspects that all
companies change how they make the decision regarding which compounds come forward
“every few months, because nobody knows what they are doing.” Our admittedly limited insight
here is to begin with the major boundaries of knowledge management: technology and science,
business and science, and all three with the human body. These, we suggest, are the most
problematic knowledge boundaries, and will take the most energy and effort to manage
effectively (Carlile 2004).
We predict that all three learning processes discovered here also exist in other science
based industries. In fact, a more focused analysis of the science versus technology and business
boundaries in mature science industries such as chemicals or materials might reveal some
preliminary insights for managing drug discovery, while the challenges of drug discovery might
reveal important transformations necessary in the mature science industries. Drug discovery and
pharmaceuticals more generally are in transformation, partly because of the productivity
declines, partly because of fundamental pressures in global health care and public policy, and
partly because of the possible convergence of now disparate components of health care
technologies – devices along with drugs as part of distinct clinical facilities based on personal
genetics, for example. We have provided a snapshot of the underlying learning processes and
the challenges of managing them that will most likely persist no matter how the industry evolves.
36
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Figure 1: Pharmaceutical Drug Discovery and Clinical Development
Drug Discovery
Target
Discovery
Informatics/
Functional
Genomics
Lead
Discovery
Medicinal
Chemistry
Cellular and
Molecular
Pharmacology
Preclinical
Development
Target
identification
Bioinformatics
Assay
development
Library
Development
In vitro drug
activity
Pharmacokinetics
Target
validation
Genomics
High
throughput
screening
Structurebased drug
design
Cellular Disease
Models
In vivo
Pharmacology
Assay
development
Proteomics
Biochemistry
and
enzymology
Medicinal
chemistry
Drug Mechanism
of Action
Tox/ Safety
Pharmacology
Key activities during drug discovery, from ideas to molecules
NOTE that is figure oversimplifies the steps, since the very real challenges of formulation (designing modes for
getting the drug into the body) and of developing “scale-up” amounts of the molecule to make enough for additional
testing and to verify that it can be manufactured in a very stable, reliable manner, are not included.
Clinical Development
39
Phase I
Phase IIa
Phase IIb
Phase IIIa
Phase IIIb
Phase IV
Safety and
tolerance of
drug
Proof of
concept
Determinatio
n of active
dose
Pharmacokin
etics
parameters
Final
decision on
formulation
Double blind
trials versus
comparators
Efficacy (1
dose) on
limited
number of
indications
vs. one
comparator
Extension of
indications
(e.g., quality of
life,
comparison to
other marketed
therapeutics)
Long term
safety and
efficacy of
launched
product
ADME:
adsorption,
distribution,
metabolism,
and excretion
Tens of
patients
Hundreds of
patients
Thousands of
patients
Small
population of
healthy, paid
volunteers
40
APPENDIX
The drug discovery and development process1
Drug Discovery and Development
Drug discovery and development is the process of creating and evaluating drugs for the safe and
effective treatment of human disease. Today, this process requires biological, chemical and
informatics expertise. The role of biology in drug discovery is primarily focused on the early
stages of research, including understanding the mechanism of diseases, identifying potential
targets for therapeutic intervention and evaluating potential drug candidates. The role of
chemistry in drug discovery is the actual invention of safe and effective new chemical entities, or
drug candidates, to address these targets. The role of informatics in drug discovery is focused on
improving decision-making by identifying and replicating the characteristics of successful drugs,
efficiently sharing current knowledge and creating databases to predict future clinical success.
Drug discovery and development comprises:
Target identification
Targets are proteins that may play a fundamental role in the onset or progression of a
particular disease. Biologists identify targets against which chemists create drugs. Until
recently, pharmaceutical researchers were limited to studying approximately 500
biological targets. The number of available biological targets is being vastly expanded
through genomics. Pharmaceutical and biotechnology companies are advancing many of
these newly identified potential targets into drug discovery. Many other potential targets
have yet to be validated, meaning that their roles in causing disease are not completely
understood.
Drug Discovery
Drug discovery includes structural biology, lead generation, lead optimization and
process research and development:
Structural biology
Structural biology is the process of cloning, expressing and purifying proteins to create
information about their functions and how they interact with drug candidates.
Lead Generation
Lead generation is the process of identifying potential drug compounds, or leads, that
interact with a target with sufficient potency and selectivity to warrant further testing and
refinement as possible drug candidates. During lead generation, researchers develop tests,
called assays, to screen libraries of leads against targets to evaluate their therapeutic
value.
1
Source : www.arraybiopharma.com/discovery/index.cfm
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Lead Optimization
Lead optimization is the complex, multi-step process of refining the chemical structure of
a compound to improve its drug characteristics with the goal of producing a preclinical
drug candidate. Researchers focus on a number of considerations in optimizing a drug
candidate, including the following drug characteristics:
Potency
The amount of a drug required to effectively treat the disease;
Selectivity
The extent to which a drug interacts only with the target; the greater the
selectivity, the lower the probability of harmful side effects;
Toxicity
The presence and significance of any harmful side effects;
Metabolism
How rapidly the drug works and how long it stays effective; and
Formulation
How the drug is administered to patients, for example, orally or by injection.
Process Research and Development
The process to make compounds for screening in lead generation typically uses a parallel
synthesis approach to explore drug characteristics, rather than to optimize ease of
synthesis, and usually results in small, milligram quantities of the compound. Before a
drug candidate can be taken into clinical trials, kilogram quantities often must be
synthesized. The goal of process research is to streamline the synthesis of larger
quantities of the compound, typically by minimizing the number of synthetic steps and
reducing the time and cost of production.
Preclinical Development
Prior to human clinical testing, a potential drug candidate must undergo extensive in
vitro, or laboratory, and in vivo, or animal model, studies to predict human drug safety.
These studies investigate toxicity over a wide range of doses and the mechanism by
which the drug is metabolized. The objective of preclinical testing is to obtain results that
will allow a drug candidate to enter human clinical trials through approval of an IND by
the Food and Drug Administration.
Clinical Development
Clinical trials, or human tests to determine the safety and efficacy of potential drug
candidates, are typically conducted in three sequential phases, although the phases may
overlap. Successful clinical trials will result in the filing of a New Drug Application, or
NDA, with the FDA in order to obtain approval to market the drug in the United States.
Similarly, clinical trials must be conducted and regulatory approvals secured before a
drug can be marketed in other countries.
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