Internet Info 1997 December/Internet_Info_CD-ROM_Walnut_Creek_December_1997.iso/nren/hpca.1991/wold.txt

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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.