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Building a Workforce for the Information Economy Appendixes

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Building a Workforce for the Information Economy This page in the original is blank.

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Building a Workforce for the Information Economy APPENDIX A Biotechnology A.1 A SHORT HISTORY OF BIOTECHNOLOGY Biotechnology dates to ancient times, having been applied to the making of bread by the early Egyptians, the selective breeding of animals (sheep, dogs, cattle), and the domestication of grains. The first major scientific underpinning of biotechnology emerged in 1953, when James Watson and Francis Crick published their paper describing the double helix structure of DNA. In the 1970s and 1980s, biotechnology focused primarily on the production of therapeutic proteins using recombinant DNA technology. In 1970, restriction nucleases were identified, opening the way for gene cloning. Three years later, genetic engineering techniques were developed for isolating and inserting DNA into bacteria so as to reproduce that DNA. Genentech, one of the first biotechnology companies, was started in 1976 with the goal of cloning human insulin. Genentech eventually licensed the human insulin technology to Eli Lilly, and in 1982, human insulin became the first recombinant DNA drug approved by the Food and Drug Administration (FDA). In 1985, Genentech became the first biotechnology company to market its own biopharmaceutical product—a growth hormone for treating children with growth disorders. In 1995, the first complete genomic sequence —for Hemophilus influenzae— NOTE: As noted in the preface, the report addresses biotechnology as a less detailed point of comparison with information technology. Where appropriate, insights from this appendix have been inserted into the main body of the report.

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Building a Workforce for the Information Economy was identified. In 2000, a rough draft of the DNA sequence of the human genome was announced. A.2 BIOTECHNOLOGY TODAY AND TOMORROW Modern techniques of cell and molecular biology, combined with capital investment from government, venture capital funds, and the pharmaceutical industry, have helped to create an important new industrial sector, biotechnology. Biotechnology has close ties with academia, which contributes important fundamental research in the underlying science and engineering and also educates a skilled biotechnology workforce. In a recent report, the Biotechnology Industry Organization suggests that biotechnology encompasses a collection of technologies—namely, monoclonal antibody, cell culture, biosensor, genetic modification, antisense, and protein engineering technologies—as well as genomics and informatics. It defines the “new” biotechnology as the use of cellular and molecular processes to solve problems or make products.1 Currently the main applications of biotechnology are in medicine, agriculture, industrial processes, and environmental management. By the end of the 1990s, genomics (the use of DNA sequence information to analyze gene products and their biological interactions) and proteomics (the use of databases of protein sequence and structure information to derive general principles of the biological functions of proteins) had begun to change the biotechnology industry. The assembly of the first working drafts of the entire human genetic code announced at the White House in June 20002 is likely to have an enormous impact on medicine by enabling development of the following: New genetic diagnostic products, A new class of genetically defined drugs of improved specificity of function, New cures for monogenic and polygenic diseases, A better understanding of how drugs can be targeted (pharmacogenomics), and New techniques for repairing and regenerating damaged tissues and organs. 1   Biotechnology Industry Organization. 2000. “What Is Biotechnology?” Washington, D.C., May 10. Available online at <http://www.bio.org/aboutbio/guide2000/whatis.html>. 2   Weiss, Rick, and Justin Gillis. 2000. “Teams Finish Mapping Human DNA.” Washington Post, June 27, p. A01.

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Building a Workforce for the Information Economy In addition, it is probable that tomorrow's health care will emphasize the prediction and prevention of disease as well as interventional treatments and the maintenance of health and well-being based on knowledge of individuals' genetic characteristics and understanding of their predisposition to disease and the risk factors that affect its staging. Modern molecular genetics, combined with robust statistical and epidemiological methodologies, is being applied to discover the relationships between gene expression and the environment and the influence of gene expression on the onset and progression of disease. With the proliferation of information at the molecular level, chromosomal aberrations are being uncovered in large numbers. For example, a single nucleotide polymorphism, detected through variations in the sequence for a disease-associated gene, may have functional significance in the body and affect an individual's susceptibility or predisposition to the associated disease. Information from polymorphisms also allows scientists to map a gene 's location on a chromosome. The integration of chromosome structure data, pharmacological and epidemiological data, and clinical trial response data is required for precise analyses of chromosomal function and aberrations. Such integration is an enormous future task for science, requiring vast computing power and advances in data linkage, mining, and imaging. Data processing skills relevant to biological data (i.e., bioinformatics) have thus become crucial to health care and the success of the pharmaceutical and biotechnology industry. An immediate challenge for genomics is to correlate gene structure and assembly with gene function and purpose. Much effort will be devoted to defining the genetic components in regulatory pathways responsible for controlling gene expression and to characterizing the biological functions of the proteins that genes encode. This will entail, among other things, a convergence of expertise in such fields as molecular biology, (patho)physiology, protein chemistry, and bioinformatics. A.3 BIOINFORMATICS AND IT TOOLS FOR BIOTECHNOLOGY An emerging and increasingly important nexus between biology, bio-technology, and information technology is bioinformatics. Genomics is generating large quantitites of new, high-quality information about complex biological organisms. In addition, computational biology, which uses simulation and computational techniques to define and analyze biological events at the level of the whole organism and, increasingly, at the molecular level, is becoming a central part of biomedical science. To

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Building a Workforce for the Information Economy assist their research and help with analyses of the results, biotechnologists rely increasingly on sophisticated IT systems with a range of capabilities. Bioinformatics focuses on the requirement to analyze, archive, and annotate the vast amounts of data contained in the great diversity of genomic and proteomic datasets.3 It is a key factor in the progress of biotechnology research and its application to the development of useful products, including gene-defined medicines and diagnostic tools for assessing disease and its etiology. Robust and scalable computer resources and technologies are needed to formulate and manage datasets placed in large repositories (libraries) of sequence, microarray, gene expression, and clone data. A challenge for IT is to link these heterogeneous datasets and databases in a coherent and navigable form. Furthermore, the volume and diversity of genomic data require that new tools be developed for data analysis. 4 Data mining and knowledge discovery require the application of often-new algorithms for clustering tens of thousands of independent data points automatically to enable the discovery of trends and patterns in large databases of unstructured information and thus help to identify novel relationships. Data mining can automate the identification, extraction, and normalization of information from unstructured text such as technical papers or genetic sequence datasets. One application of data extraction —the automatic population of a database of protein structure and function that is generated by an automated search of the scientific literature —has been technically validated at the National Library of Medicine using the NLM's text processing capabilities (Metamap). In addition to data extraction applications, text processing can be used to cluster and summarize large document sets, although new capabilities in natural language processing and text processing are required. Also needed are means to facilitate the integration of gene-based data from a wide variety of private and public data sources such as tissue banks, epidemiological databases, and clinical trials so that scientists and clinicians can develop, for example, a comprehensive picture of the progression of a particular disease. Biotechnologists also have an increasing need to organize and search the scientific literature. Indeed, many biotechnology and biomedical workers can spend large portions of their working time searching for 3   Bioinformatics is not synonymous with biomedical computing or medical information sciences. Bioinformatics focuses on a computational approach to handling data and is arguably a subspecialty of biomedical computing, which may also include biomedical imaging, information retrieval and analysis for medical decision making, and so on. 4   Baldi, Pierre, and Søren Brunak. 1998. Bioinformatics: The Machine Learning Approach. Cambridge, Mass.: MIT Press.

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Building a Workforce for the Information Economy scientific information. A related issue is that, because the volume of information is increasing so rapidly, the time from discovery to publication is forcing editors and researchers to rethink the traditional methods of publishing in printed journals so that publication times can be reduced. New electronic methods are being discussed. Other advances in IT related to biotechnology include the development of models that simulate cell processes and the linkage of visual and digital images of physiological and genetic processes. In addition, security and privacy technologies are receiving more attention as confidential personal and commercial data are collected, stored, transmitted, and used in the development and marketing of new products. A.4 IMPACT OF BIOTECHNOLOGY ON THE ECONOMY A recent report points out that the biotechnology industry has already had a significant impact on the U.S. economy, having doubled in size between 1993 and 1999.5 According to the report, the biotechnology industry was responsible in 1999 for the following: 437,400 U.S. jobs. Of these, 150,800 were generated directly by biotechnology companies and 286,600 were generated by companies supplying inputs to the industry or by companies providing goods and services to biotechnology employees. $47 billion in revenue. Biotechnology companies produced revenues of $20 billion, and companies supplying inputs or selling goods and services to biotechnology employees produced revenues of $27 billion. $11 billion in R&D spending. A.4.1 Biotechnology in Drugs, Medical Diagnostics, Agriculture, Environmental Management, and Manufacturing Biotechnology is a global industry. The United States, Europe, and —to a lesser extent—Australia and Asia provide much of the science and innovation required to develop effective products and processes. Biotechnology spans human and animal health care, agriculture, the industrial manufacture of proteins, and management of the environment. In each of these broad areas, modern biotechnology methods and techniques 5   Ernst & Young Consulting and Quantitative Analysis. 2000. The Economic Contributions of the Biotechnology Industry to the U.S. Economy. Washington, D.C.: Biotechnology Industry Organization.

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Building a Workforce for the Information Economy are leading to new classes of products and enabling processes. For example, in human health care, biotechnology companies are developing, in addition to recombinant therapeutic proteins for drug and diagnostic use, drugs based on genetic information, methods to create patient biosignatures for diagnostic purposes, new manufacturing processes using cells as production factories, and cell and gene therapies. Health care, including drugs, vaccines, diagnostics, and related products, is the focus of about 55 percent of the biotechnology industry, and employees in such health-related companies account for more than 65 percent of the employees in U.S. biotechnology companies. A.4.2 Number of Companies and Their Valuation In 1999, the U.S. biotechnology industry included 327 public companies with 106,000 employees and had total revenues of $15 billion and a total net loss of $2.7 billion (Table A.1). Many of the companies provide larger biotechnology and pharmaceutical firms with support in genomics, combinatorial chemistry, high-throughput screening, and bioinformatics. Although several have positive cash flows, only about two dozen are currently profitable. Private and public companies in the U.S. biotechnology industry in 1999 together totaled 1,283 and had 153,000 employees, total revenues of $18.6 billion, and a total net loss of $5.1 billion. By contrast, Merck—a large, mature pharmaceutical company—had 53,800 employees in 1998, total revenues of $24 billion, and a net income of $4.6 billion (see Table A.1). A.4.3 Relationship to the Pharmaceutical Industry The biotechnology industry has strong connections to the much larger pharmaceutical industry, which provides capital in return for access to new drug technologies, thus playing a role similar to that of venture capitalists in the IT industry. Close technical and business collaboration between the biotechnology and pharmaceutical industries has led in recent years to a melding of employee skills and competencies across the two. Today's pharmaceutical industry is characterized by work teams, techniques, and processes that are often indistinguishable from those in the biotechnology industry. It appears that over time, biotechnology will increasingly become an enabling technology for pharmaceutical as well as agricultural products companies, which will have a greater need for inhouse biotechnology competency as corporate alliances mature.6 Estimates 6   Mark D. Dibner, president, Institute for Biotechnology Information, private communication, June 2000.

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Building a Workforce for the Information Economy TABLE A.1 U.S. Biotechnology Companies in 1999 Compared with a Large Pharmaceutical Firm in 1998 (billion dollars)   Public Biotech Companies (1999) All Biotech Companies (1999) Merck (1998) Revenues 15.2 18.6 24.0 R&D expenses 6.2 9.9 1.7 Net income (loss) (2.7) (5.1) 4.6 Market capitalization 91.2 97.0 162.0 Number of Employees 106,000 153,000 53,800 SOURCE: Ernst & Young. 2000. Biotech 99: Bridging the Gap. Biotechnology Industry Annual Report, pp. 4 and 7. Available onlineat <http://www.ey.com/global/vault.nsf/International/Biotech99:BridgingTheGap/$file/biotech99.pdf>. suggest that the number of biotechnology employees within the pharmaceutical industry has more than doubled over the past 5 years. While the pharmaceutical industry has provided much of the financial underpinning for biotechnology companies, venture capital, government grants, and financing from the public markets have also been major factors in the success of the biotechnology industry. Another factor has been employee participation in equity sharing and its contribution to individual wealth generated by public offerings of company stock. A.4.4 Global Competition: The United States and Europe The U.S. biotechnology industry leads or is competitive with the rest of the world in terms of its size and the development of innovative products and processes. It can be argued that much of its success derives from early investment by venture capital funds, government, and the pharmaceutical industry in high-risk, cutting-edge science. During the past few years, venture capital funds have made additional investments in Europe's biotechnology industry as academics and governments there adopt a more entrepreneurial approach to the exploitation of basic scientific findings. In 1996, the U.S. biotechnology industry was at least four times larger than the European biotechnology industry in terms of revenues, R&D expenses, and number of employees but only twice as large in terms of the number of companies (Table A.2). The U.S. strategy of focusing on the creation of new products and processes has thus far provided a competitive edge over the Japanese strategy of licensing existing products, focusing on production technologies,

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Building a Workforce for the Information Economy TABLE A.2 A Comparison Between the European and the U.S. Biotechnology Industries, 1996   Europe United States Companies 716 1,287 Employees 27,500 118,000 R&D expenses, billion ecu 1.5 6.3 Revenues, billion ecu 1.7 11.7 SOURCE: Animal Cell Technology Industrial Platform (ACTIP) positionpaper on key figures of the European biotechnology industry, availableonline at <www.actip.org/manuals/keyfigures.html>. The ACTIP is an informal forum of European companies with activitiesin animal cell technology. and investing modestly in basic research.7 Information published in 1998 estimated the worth of the Japanese biotechnology market at about ¥1,083 billion (about $9 billion).8 A.5 WORKFORCE ISSUES A.5.1 Growth in the Workforce In 1999, the number of people working in the biotechnology industry in the United States was estimated to be 153,000, a 9 percent increase over the preceding year.9 Total employment now is estimated at approximately 172,000. Employment has been growing at between 9 and 17 percent per year over the past 4 years and is expected (conservatively) to increase annually by 8.5 percent for the next decade. Over the next 4 years, the number of commercially available products is likely to double from the current total of about 110. In addition, there are 1,100 to 1,800 products in the pipeline—both preclinical and clinical. Of these, about 400 are in the late phases of clinical trials, and it is estimated that about a third of them could become viable and successful commercial products.10 7   Callan, Benedicte. 1996. “Why Production Technology Is Not a Measure of Competitiveness in the Biotechnologies,” BRIE Working Paper 86. Berkeley, Calif.: University of California. Available online at <http://brie.berkeley.edu/~briewww/pubs/wp/wp86.html>. 8   BioIndustry Association. 1998. “Biotechnology in Japan–Review,” prepared by Euro Japan Marketing Limited (Tokyo), April. 9   Ernst & Young. 2000. Biotech 99: Bridging the Gap. Biotechnology Industry Annual Report, Palo Alto, Calif. Available online at <http://www.ey.com/global/vault.nsf/International/Biotech99:BridgingTheGap/$file/biotech99.pdf >. 10   Steve Dahms, San Diego State University, interview March 8, 2000.

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Building a Workforce for the Information Economy A.5.2 Training and Education The range of expertise needed to develop biotechnology products or processes includes molecular and cell biology, bioengineering, testing, pharmacology, toxicology, process scale-up, clinical research and development, formulation science, regulatory affairs, and manufacturing. Increasingly, the scientific base of biotechnology rests on genetic information developed through the activities of genomics and proteomics, aided by bioinformatics. A strong—and growing—scientific base drives product development in biotechnology. (By contrast, the scientific underpinnings of information technology products are sparser, in the sense that science per se does not drive the solution of user problems.) Perhaps as a result, the general biotechnology workforce responsible for product development requires a higher level of education than does the analogous IT workforce (e.g., biotechnology employs a greater proportion of Ph.D.s). In most cases, biotechnologists must have years of training in experimental work. At least half of biotechnology industry employees hold scientific and technical positions and are involved in R&D or production.11 Of these, a typical categorization might be as follows: 19 percent have a Ph.D., 17 percent have an M.S., 50 percent have a B.S., and 14 percent are prebaccalaureate or community college educated.12 For the smaller, more research-intensive companies, the proportion of employees with scientific and technical skills may be even higher. For example, 85 percent of the staff of Geron, a 100-employee company, have scientific and technical positions, and more than half have advanced degrees.13 At Millennium Pharmaceuticals, a company with approximately 1,500 employees, 95 percent of the staff have college degrees, with 60 percent holding advanced degrees, half of which are at the M.D. or Ph.D. level. Beyond their training in basic science, professionals in the biotechnology workforce require considerable in-house training and education to develop the ability to work on project teams. Furthermore, close interdisciplinary teamwork necessitates a working knowledge of other (sub)disciplines. As a rule, new recruits into the industry do not have such a range of experience. These comments notwithstanding, formal subbaccalaureate education is also an important source of skills for biotechnology workers ( Box A.1). 11   Dibner, Mark D. 1999. “Career Alternatives for Scientists.” Nature Biotechnology 17(8):825. 12   Steve Dahms, San Diego State University, interview March 8, 2000. 13   Based on testimony presented at the meeting of the Committee on Workforce Needs in Information Technology, September 22-24, 1999, Santa Clara, Calif.

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Building a Workforce for the Information Economy BOX A.1 Community College Programs in Biotechnology Biotechnology courses at community colleges fulfill several needs. They provide hands-on experience for those with the relevant scientific qualifications, opportunities for career change (often for people with B.S. degrees), and practical skills for individuals who want to go immediately into industry or move to a 4-year college. Anecdotal information suggests that individuals who complete these programs do not have much difficulty finding jobs in the biotechnology industry, which—if broadly true—implies that they do not experience the difficulties in job hunting perceived by retrained IT workers. Bio-Link is a National Science Foundation Advanced Technological Education (ATE) Center whose charge is to support new programs and faculty development in community colleges, as well as linkages between community colleges and high schools and between community colleges and 4-year colleges. Bio-Link's national center is at the City College of San Francisco, located at the University of California at San Francisco. Bio-Link also has six regional centers: Seattle Central Community College, in Washington state; San Diego City College, in California; Austin Community College, in Texas; Madison Area Technical College. in Wisconsin; Alamance Community College, in North Carolina; and New Hampshire Community Technical College. Collaborative networking efforts under the auspices of Bio-Link and similar programs include collaborations such as the following: High school and community college. A partnership between San Diego High School and San Diego City College resulted in a 3-year biotechnology course in the vocational program at the high school. Year 1 covered DNA; year 2, proteins; and year 3, a research project and an industrial internship. Laboratory space is provided by the college. Community college and both high schools and 4-year colleges. Austin Community College (ACC) seeks to create a pipeline from high school through the community college to the 4-year college. ACC has helped two high schools set up biotechnology courses by providing equipment and training the teachers. An Industry Board from the local biotechnology industry provides input to ACC on the organization of the biotechnology courses, the material covered, when the courses are held, and so on. Community college and 4-year college. The Massachusetts Bay Community College operates three biotechnology courses—general biotechnology, marine biotechnology, and forensic DNA science. These courses prepare students for careers as research technicians and for the pursuit of advanced degrees. Eighty percent of the graduates work toward higher degrees, and the rest go directly into industry. 4-year college and community college. The Department of Biological Sciences at the Rochester Institute of Technology seeks students from community colleges and has articulation agreements with the New Hampshire Community Technical College, the City College of San Francisco, and the Finger Lakes Community College, in Canandaigua, New York. Students from these colleges can enter the junior year at Rochester Institute of Technology if their GPA is at least 2.75, if they are students in good standing, and if they are supported by the program director.

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Building a Workforce for the Information Economy A.5.3 Foreign Worker Participation The biotechnology industry, like the IT industry, has used H-1B visas to recruit staff in areas of skill shortages (see above). According to data collected by Steve Dahms, of San Diego State University, the use of H-1B visa holders is much higher in Southern California 's biotechnology industry than first thought. Of the 25,000 employees who work in San Diego area biotechnology and biopharmaceutical companies, approximately 6.4 percent are H-1B visa holders. (One research-intensive biotechnology company, Millennium Pharmaceuticals, reported that 17 percent of its workforce is composed of foreign nonimmigrant workers and that about 3 percent of its staff are permanent residents whom Millennium has sponsored to achieve residency status.) For comparison, among IT workers in the San Diego area 650 out of 10,200 employees at Qualcomm and 321 out of 9,800 employees at SAIC are H-1B visa holders. A.5.4 Age of Workers and Hiring Needs According to National Science Foundation SESTAT data (data from surveys on science and engineering personnel), the mean age of those with bachelor's or higher degrees working in the private biotechnology industry (excluding those in academia and in government) is about 37.4 years, comparable to the mean age of the U.S. workforce at large. Given the life cycle of many biotechnology products (especially those that must clear various regulatory hurdles such as those imposed by the FDA), biotechnology employers place a high value on experienced workers who understand the drug development process and are also experienced managers. As a rule, such workers develop their experience on the job and tend to have spent many years in the industry. In the areas of the United States where the biotechnology industry is most heavily concentrated—in the Northeast (the Boston to North Carolina corridor) and California (San Francisco, San Diego)—employer competition for employees is described as being very keen. Despite an oversupply of life sciences Ph.D.s,14 the biotechnology industry is having difficulty finding Ph.D.-level specialists in analytical chemistry, instrumentation, organic synthesis, clinical biostatistics, bioinformatics, production quality assurance, and regulatory affairs.15 Managers 14   National Research Council. 1998. Trends in the Early Careers of Life Scientists. Washington, D.C.: National Academy Press. 15   Steve Dahms, San Diego State University, interview on March 8, 2000, and testimony at the meeting of the Committee on Workforce Needs in Information Technology, September 22-24, 1999, Santa Clara, Calif.

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Building a Workforce for the Information Economy with good skills in project definition and management are also in short supply. And, because of the long product cycles and heavy regulatory burden imposed on pharmaceuticals, individuals with experience in the later stages of drug development are highly prized. A particularly challenging area is bioinformatics. The wealth of biotechnology-related data continues to expand, along with the need to analyze and understand it, and specialists in bioinformatics— arguably a subspecialty in IT work—are now in great demand relative to supply. Other efforts that will increase the need for managing large amounts of information include rational drug design and the conduct of genotype-profiled clinical trials. Such efforts will shorten development and testing times and facilitate targeting of drug delivery more precisely to the people that would benefit. There is no sign that the demand for bioinformatics specialists is abating.16 Indeed, the demand will continue to grow rapidly, given estimates that as many as 40 percent of the biotechnology companies that survive will be selling information rather than products.17 A.5.5 Additional Opportunities and Challenges In the late 1990s Harold Varmus, then director of the National Institutes of Health, asked his advisory committee “to assess the challenges and opportunities presented to the National Institutes of Health by the convergence of [biomedicine and IT].”18 The result was a mid-1999 set of recommendations for “national programs of excellence in biomedical computing,” including cross-disciplinary education and training as well as institutional support for cross-disciplinary research. In the newer fields of computational biology, genomics, and bioinformatics, several distinct challenges confront the biotechnology workforce. First, users of genetic sequence data and databases, image repositories, and laboratory cell data are generally not highly trained informaticians. They have studied biology and molecular biology and generally require instruction in how to use IT tools. Second, knowledge-based expert systems are in demand to support clinicians in their decision 16   Paula Stephan, “Bioinformatics: Emerging Opportunities and Emerging Gaps,” presented at the Government-Industry Partnerships in Biotechnology and Computing Conference, sponsored by the Board on Science, Technology, and Economic Policy, National Research Council, Washington, D.C., October 25-26, 1999. 17   Steve Dahms, testimony at the meeting of the Committee on Workforce Needs in Information Technology, September 22-24, 1999, Santa Clara, Calif. 18   Working Group on Biomedical Computing, Advisory Committee to the Director. 1999. “The Biomedical Information Science and Technology Initiative.” National Institutes of Health, June 3.

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Building a Workforce for the Information Economy making and to help them make effective use of scientific knowledge. Training that links biologists and computer scientists is critical to meeting these needs. New authoring tools are required to allow researchers, clinicians, and educators to work with complex data without having to learn complex information models or structures. The educational needs of those who have not studied genetics or used genetic principles in their work will be magnified as more clinicians begin to prevent, detect, diagnose, and treat diseases on an individualized basis. As biotechnology products become more common in the marketplace, individuals qualified for sales and marketing will see additional employment opportunities; such positions are not likely to require postgraduate education in the life sciences. Nevertheless, the industry and its products are driven by scientific discovery, and scientific reasoning and experience are vital to the discovery process in biotechnology. In IT, by contrast, individuals without college degrees can often create and develop successful products. A.6 SIMILARITIES IN AND DIFFERENCES BETWEEN THE BIOTECHNOLOGY AND IT INDUSTRIES The biotechnology industry is similar to the IT industry in a number of key dimensions. Both are leaders in scientific innovation. Both enjoy a close relationship among their venture capital, industrial, academic, and government components. Advances in each industry are driven by R&D (e.g., cloning in biotechnology, object-oriented programming in IT), although the biotechnology industry spends a greater proportion of its revenues on R&D than does the IT industry. Both industries are relatively young in comparison with physics and most engineering fields, so they share the advantages and disadvantages of relative youth. Both are entrepreneurially driven, with great importance attached to the availability of venture capital at the front end of the process and the hope for substantial market capitalization. And both industries place a very high premium on speed, as evidenced by IT in its concern over “time to market” and biotechnology in its concern to be “first to invent” and “first to patent,” as well as to lead in time to market. There are also key differences. Compared to IT product development, product development in biotechnology is more risky, more costly, and generally more time consuming. Unlike the IT industry, the biotechnology industry is highly regulated: for example, the FDA regulates drugs, foods, cosmetics, diagnostics, medical devices, and animal and human food additives, and the USDA regulates animal vaccines, plant pests and derivatives, and transgenic plants and animals. While biotechnology spans the development of products with very short life cycles

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Building a Workforce for the Information Economy (e.g., genomics information that is sent business to business) and products with long life cycles (e.g., medicines), for most products, the time to market can be very long (several years). Also, because of the high degree of attrition in developing a clinical product, the success rate for the biotechnology industry is about one in ten, and the averaged cost of bringing a successful therapeutic product to the market represents an investment of around $500 million. The capital investment needed to pursue biotechnology, especially in the initial stages, is much higher than that for much of the IT industry (chip fabrication aside). Additionally, biotechnology has a much longer history of protecting its innovative efforts by creating barriers to entry (proprietary position/relationships and, especially with the pharmaceutical industry, patents) than does the IT industry, although the IT industry is rapidly turning to patents to protect its innovations as well. There are interesting cross-plays between the biotechnology and IT sectors. IBM has estimated that drug companies are spending $1.2 billion to $1.8 billion externally on R&D software and IT equipment. As pharmacogenomics and molecular medicine expand, and as clinics and providers move into individualized medicine, the IT opportunity expands alongside them. IBM has announced that its Gene Blue project—the company aims to build a computer 500 times more powerful than the fastest computers used today—will attempt to solve the complex problem of protein folding, a key challenge in understanding protein function. Also, in response to the needs of the biotechnology industry, several new companies are using the Internet to create new markets for bioinformatics and are offering easy-to-use versions of complex software to life scientists rather than to bioinformaticians per se.