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Supercomputing Network: A Key to U.S. Competitiveness
in Industries Based on Life-Sciences Excellence
John S. Wold, Ph.D.
Executive Director
Lilly Research Laboratories
Eli Lilly and Company
Testimony
U.S. Senate, Commerce, Science and Transportation Committee
Science, Technology and Space Subcommittee
March 5, 1991
I am John S. Wold, an executive director of Lilly Research
Laboratories, the research-and-development division of Eli Lilly and
Company. Lilly is a global Corporation, based in Indianapolis, Indiana,
that applies advances in the life sciences, electronics, and materials
sciences to basic human needs -- health care and nutrition. We
compete in the pharmaceutical, medical-devices, diagnostic-products,
and animal health-products industries.
My responsibilities at Lilly include the company's supercomputing
program. With me is my colleague, Dr. Riaz Abdulla -- whom you just
saw on videotape. Riaz manages this program on a day-to-day basis.
I'm indeed pleased to have this opportunity to present my
company's views about the importance of a national commitment to
supercomputing and to a supercomputing network.
I'm sure that this subcommittee has heard -- and will hear much
more -- about the underlying technology required to support the
evolution of supercomputers and supercomputing networks. It's
important, I believe, that you share computing technologists'
excitement about their visions of supercomputing systems,
algorithms, and networks. But I believe it is just as important for you
to share the visions that motivate research-oriented institutions, like
Lilly, to invest in supercomputers and to encourage their scientists
and engineers to use these systems. It's important for you to hear
supercomputer users support S. 272.
Today, I'll try to articulate two levels of aspirations we at Lilly have
for our supercomputing program:
- First, we believe that Lilly scientists will use these powerful
new research tools to address fundamental research questions.
Answers to these questions will help us develop more-selective,
more-specific drugs with greater efficacy and fewer side effects.
These new medicines will represent important new products for our
company and support high quality, cost-effective health care for tens
of millions of people.
- Second, we believe that Lilly scientists will use these powerful
new research tools to expand the range of fundamental questions
they can explore. They may even use these systems to devise
entirely new ways of conducting research programs that probe the
staggering complexity of the human body.
In fact, supercomputing represents a revolution...a new wave...a
"paradigm shift" in the development of modern technology. In the
years ahead, scientists at Lilly and at other institutions will use this
extraordinary research tool to do things that we simply cannot
anticipate today. For instance, it's unlikely that pioneers of molecular
biology foresaw the applications of recombinant DNA technology that
have unfolded in the past I5 years or so.
Let's move, however, from the general to the specific. I'd like to
discuss supercomputing in the context of one company's decision.
making.
The investment by Eli Lilly and Company of millions of dollars in
supercomputing systems and training was a very basic business
decision. We believe that this technology will help us effectively
pursue our company's mission and meet its goals in. an ever-more
challenging environment. Today, I'll focus on our pharmaceutical
business. But many of the following points are also relevant to our
other businesses.
Long-term success in the research-based pharmaceutical industry
depends on one factor: innovation. We must discover and develop
new products that address patients' unmet needs. We must discover
and develop cost-effective new products that offer economic benefits
to patients, payors, and society as a whole. Whenever possible, we
must market innovative new products before our competitors do.
Innovation has never come easy in this industry. The diseases that
afflict our species represent some of the most daunting of all
scientific mysteries. Consequently, pharmaceutical R&D has
traditionally been a high-risk...complex... time-consuming...and costly
enterprise.
How risky is pharmaceutical R&D? Scientists generally evaluate
thousands of compounds to identify one that is sufficiently promising
to merit development. Of every five drug candidates that begin
development, only one ultimately proves sufficiently safe and
effective to warrant marketing.
The risk does not end there, however. A recent study by Professor
Henry Grabowski, of Duke University, showed that only 3 of 10 new
pharmaceutical products introduced in the United States during the
1970s actually generated any profits for the companies that
developed them.
How complex is pharmaceutical R&D? Consider just some of the
hurdles involved in the evaluation of each potential pharmaceutical
product that enters the development process:
- We must complete scores of laboratory tests that probe potential
safety and efficacy.
- We must manage global clinical tests of safety and efficacy that
involve thousands of patients in a dozen or more countries.
- We must formulate dosage forms of each product that best deliver
the active ingredients to patients.
- We must develop high-quality, cost-effective, environmentally
sound manufacturing processes for compounds that are often very
complex chemical entities.
- We must prepare mountains of research data for submission to
regulatory authorities in countries around the world. For instance,
one of our recent submissions to the U.S. Food and Drug
Administration involved 900,000 pages of data assembled in well
over 1,000 volumes.
How time-consuming are these complex R&D programs? Let's go step
by step. It usually takes several years to establish a discovery-
research program in which scientists begin to identify promising
compounds. It typically takes from 5 to 8 years for us to conduct all
the tests required to evaluate each drug candidate. Then it takes
another 3 to 4 years for regulatory authorities to consider a new
drug application and approve the marketing of the new product.
Consider this example. The Lilly product Prozac represents an
important new treatment for patients suffering from major
depressive disorder. Although we introduced Prozac to the U.S.
medical community in 1988, this innovative product came from a
research program that began in the mid-l960s. The bottom line is
that discovery-research programs often take a total of two decades
or more to yield new products.
How costly are these long, complicated R&D programs? Last year, a
Tufts University group estimated that the discovery and
development of a new pharmaceutical product during the l980s
required an investment of some $231 million in 1987 U.S. dollars.
That number is increasing rapidly. One reason is the ever-more
meticulous safety testing of drug candidates in humans. In the mid-
l970s, for instance, clinical trials of the Lilly oral antibiotic Ceclor
involved 1,400 patients. But recent clinical studies of our oral-
antibiotic candidate Lorabid encompassed 10,000 patients. Clinical-
trial costs constitute the largest portion of total drug-development
expenses -- and they have skyrocketed in recent years.
At Lilly, we believe that it will take $400 million to develop each of
our current drug candidates. And those costs do not include the
expenses required to build manufacturing facilities -- expenses that
can climb well into nine figures for hard-to-manufacture products.
Pharmaceutical R&D has become a "big science." The R&D programs
that yield new drugs need the same kinds of technical, management,
and financial commitment required to develop the most imposing
high technology products -- including supercomputers themselves.
I want to mention another dimension of our business environment.
The research-based pharmaceutical industry is unusually
competitive and cosmopolitan. Historically, no single company has
held more than 5 percent of the global market. Based on sales, the 10
or 12 top-ranking companies are very tightly clustered, compared
with most industries. These companies are based in France, Germany,
Switzerland, and the United Kingdom, as well as in the United States.
I would like to note that many of our competitors abroad are
mammoth technology-based corporations, such as Bayer, CIBA-
GEIGY, Hoechst, Hoffman-La Roche, Imperial Chemical Industries, and
Sandoz. These are truly formidable firms with superb technical
resources. Their pharmaceutical operations represent relatively small
portions of their total sales. By contrast, U.S. pharmaceutical
companies are, for the most part, smaller companies that have
focused their resources on human-health-care innovation.
In this competitive industry, the United States has an excellent
record of innovation. For instance, nearly half of the 60 new
medicines that won global acceptance between 1975 and 1986 were
discovered by U.S.-based scientists. In addition, the pharmaceutical
industry has consistently made positive contributions to this nation's
trade balance.
Over the past half decade, however, the research-based
pharmaceutical industry has experienced major changes. The rapid
escalation of R&D costs has helped precipitate major structural
changes in a sector of the global economy where the United States is
an established leader. An unprecedented wave of mergers,
acquisitions, and joint ventures has led to fewer, larger competitors.
In several cases, foreign companies have assumed control of U.S.
firms.
Competition in the research-based pharmaceutical industry will only
become more challenging during the 1990s and beyond.
Consequently, Lilly has evaluated many opportunities to reinforce its
capacity to innovate -- to reinforce its capacity to compete.
Supercomputing is a case in point:
- We believe that these powerful systems will help our scientists
pursue innovation.
- We believe that these systems will help us compete.
Now, let's move from business to science. Scientists have long been
frustrated in their efforts to address the fundamental questions of
pharmaceutical R&D. Only recently have we been able to begin
probing these questions. We've begun to probe them not through
experimentation but through the computational science of molecular
modeling. Prominent among these scientific priorities are the
following:
- The quantitative representation of interactions between drug
candidates and drug targets, especially receptors and enzymes
- The process by which proteins -- huge molecules that are
fundamental to life -- are "folded" into distinct con- figurations
through natural biological processes
- The properties that enable catalysts to facilitate essential
chemical reactions required to produce pharmaceutical products.
Today, I'd like to discuss the first of these challenges. I'll
concentrate on the interaction of drug candidates with receptors.
As you know, normal biological processes -- the beating of the
heart, the clotting of blood, the processing of information by the
brain -- involve complex biochemical chain reactions, sometimes
referred to as "cascades."
Let me give you an example. During these chain reactions,
natural substances in the body cause certain substances in the body
to produce other molecules, which, in turn, cause either the next
biochemical step in the cascade or a specific response by an organ or
tissue -- a movement, a thought, the secretion of a hormone.
Over the years, scientists have found that disease often occurs
when there is either too much or too little of a key molecule in one of
these biological cascades. As a result, research groups are studying
these chain reactions, which are fundamental to life itself.
The natural substances involved in these processes link with, or
bind to, large molecules, called receptors, which are located on the
surfaces of cells. We often use this analogy: a natural substance fits
into a receptor, much like a key fits into a lock. Many scientists at
Lilly -- at all research-based pharmaceutical companies -- are
focusing their studies on receptors involved in a host of diseases,
ranging from depression and anxiety to heart attack and stroke.
Their goal is to better understand these locks and then to design and
to synthesize chemical keys that fit into them.
In some cases, we want to design chemical agents that activate
the receptor and stimulate a biochemical event. Compounds called
agonists serve as keys that open the locks. In other cases, we want to
synthesize chemical agents that block the receptor and stop a natural
substance from binding to the receptor. These compounds, called
antagonists, prevent the biological locks from working.
Unfortunately, this drug-design process is fraught with problems.
Most importantly, receptors are not typical locks. They are complex
proteins composed of thousands of atoms. Moreover, they are in
constant, high-speed motion within the body's natural aqueous
environment.
This brings us to one of the most promising applications of
supercomputing technology. Mathematicians can formulate
equations that describe virtually anything we experience or
imagine: the soft-drink can on your desk or the motion of the liquid
in that can as you gently swirl it during a telephone conversation.
Each can be expressed in numbers.
Of course, those examples are relatively simple. But scientists
can also develop equations that describe the remarkable complexity
of meteorological phenomena...geological formations...and key
molecules involved in the body's natural processes. In recent years,
they have developed mathematical models describing the realistic
motion -- the bending, rotation, and vibration -- of chemical bonds in
large molecules, such as receptors. These models are emerging as
important tools for scientists probing how potential drug candidates
would likely affect the target receptors.
These mathematical descriptions are based on equations
involving billions of numbers. Conventional computers take days,
weeks, or even longer to perform related calculations. But
supercomputers do this work in fractions of a second. A second
computer then translates the results into graphic representations on
a terminal screen.
These graphic representations can serve as a new
communications medium -- and new "language" -- for scientists.
Teams of scientists can share the same visualized image of how a
specific chemical agent would likely affect the receptor in question.
They can quickly evaluate the probable effects of modifications in
the chemical. They can generate entirely new ideas -- and analyze
them. They can focus the painfully slow efforts required to
synthesize and test compounds on those agents that appear to have
genuine potential.
Supercomputers enable scientists to see what no one else has
seen. Historically, technical breakthroughs that have dramatically
expanded the range of human perception -- from early telescopes
and microscopes to modern cyclotrons and electron microscopes --
have enabled the research community to make landmark discoveries,
develop revolutionary inventions, and pioneer new academic
disciplines. We have every reason to believe that supercomputing
can do the same.
Now, let's return to the Lilly experience. Several years ago, the
interest in supercomputing began to grow at Lilly Research
Laboratories. We considered a number of ways to evaluate this
research tool. Obviously, supercomputers don't do anything by
themselves. They would only be relevant to our mission and our
goals if Lilly scientists actively and creatively embraced them. We
had to see whether our biologists, chemists, and pharmacologists
could really apply those graphic representations of receptors and
enzymes to real drug-discovery problems.
In January 1988, we took the first step: Lilly became an
industrial partner in the National Center for Supercomputing
Applications (NCSA) at the University of Illinois. This opportunity to
learn about supercomputing afforded us by interacting with the
NCSA proved to be an essential element in our supercomputing
decision. Many of our scientists were in- deed interested in learning
how to use supercomputers. Many of them quickly began to apply
the systems to their work.
In April 1990, our supercomputing program took a great step
forward with the installation of a Cray 2S-2/128 system at our
central laboratories in Indianapolis. Lilly scientists are using the
system at a far greater rate than we expected. In the meantime,
we've maintained our relationship with the NCSA to ensure
maximum support for our program and to keep abreast of new
developments in the field.
Our experience to date suggests three interrelated advantages of
supercomputing that we believe will make Lilly even more
competitive in the years ahead.
- We believe these systems will speed up the identification of
promising drug candidates. Supercomputing will enable Lilly
scientists to design new drug candidates that they otherwise would
not have even considered. Supercomputing may well cut days,
weeks, even months from the overall process required to identify
novel compounds.
- We believe these systems will foster great collaboration among
scientists from various disciplines who are involved in
pharmaceutical R&D. Productive research in our industry
increasingly depends on teamwork. supercomputer-generated
graphic simulations help scientists with diverse academic training to
share the same vision of crucial data. Again, these visual images
become a common language for scientists with different academic
training.
Moreover, supercomputing will make these multidisciplinary
research efforts more spontaneous, energetic, and intense. In the
past, our research was a step-by-step process in which long periods
often separated the formulation of ideas from experiments required
to test those ideas. But supercomputing helps teams of scientists
integrate their ideas and tests into a dynamic, interactive process.
These systems facilitate the communication, creativity, and decision
making that are critical to productive R & D programs.
- We believe these systems will encourage truly visionary
exploration. A spirit of unfettered inquiry drives scientific progress.
In the past, however, scientists were unable to test many novel ideas
because they didn't have sufficient computing power. Now,
supercomputers are motivating our scientists to ask "what if?" more
boldly than ever before -- and to help them quickly consider many
possible answers to their questions.
It's especially interesting to watch scientists actually get familiar
with supercomputing. As you know, good scientists are among the
most independent people in any society. They respect good theories.
But they demand empirical data to support the theories. In six
months, I've seen some pretty tough-minded chemists move from
skepticism to genuine enthusiasm for these systems. Moreover, we
clearly see that many of the very brightest young Ph.D.s coming out
of graduate school are very enthusiastic about this technology. Our
supercomputing capabilities have become a recruiting magnet.
I want to stress that supercomputing is only one of a number of
powerful new technologies that research-based pharmaceutical
companies are applying to their drug-discovery programs. But it's a
very powerful scientific tool -- a tool that will become all the more
powerful with networking capabilities.
- A supercomputer network will greatly facilitate the dynamic
collaboration among scientists at different locations -- often different
institutions. Lilly scientists are working with research groups at
universities and high technology companies around the world. A
national supercomputer network would greatly enhance the
effectiveness of joint efforts with our colleagues at the University of
Michigan or the University of Washington at Seattle, for example.
- A supercomputer network will help us optimize scarce
scientific talent during a period when we're almost certain to
experience major shortfalls in the availability of Ph.D.- level
scientists. I would go so far as to suggest that the visualization
capabilities of supercomputing may actually help attract more of the
best and the brightest into the sciences -- this at a time when key
industries in the U.S. economy desperately need such talent.
Finally, I can't overemphasize that a supercomputing network
will help scientists ask questions whose answers they could never
seriously pursue before. Tens of thousands of our best thinkers will
find applications for this technology that will totally outstrip any
predictions that we venture today. Supercomputing represents a
revolution. a new wave...a paradigm shift in the development of
modern technology.
In conclusion, I want to stress two points. We believe that
supercomputers and a national supercomputing network are
important to our company, to our industry, and to the medical
professionals and patients we serve. We believe that super-
computing will play a crucial role in many technology-based
industries and in the growth of national economies that depend on
these industries. Again, we strongly recommend the enactment of S.
272.
Thank you.