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.