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10 1985 1990 1995 2000
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FIGURE 4.5 Desktop computer system price declines, 1987 2002.
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Source: Bureau of Economic Analysis
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they are increasingly affordable. For example, in 2001, IBM committed $100 million over ve years to computational biology, predominantly in the form of a commitment to build Blue Gene, the next generation su percomputer for genomic and proteomic research. Similarly, in 2001, Celera Genomics Corporation, Sandia National Laboratories, and Com paq entered a partnership to develop algorithms and software for life sciences research on supercomputers. For some problems, the computa tional requirements are so great that even the most powerful desktop systems aren t practical. Tasks such as protein modeling need the power of a supercomputer or grid computing to provide results in hours or days instead of the weeks or months that would be required on a desk top computer.
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Computing in Pharmaceutical Firms
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The constantly increasing performance and affordability of computing power since the late 1980s rede ned the methodologies used in scienti c research and in information-intensive industries. Many industries accepted the need to computerize on face value, before studies of return on invest ment for adopting computer technology were available. For example, many pharmaceutical rms view computing methods as fundamental to the drug development process, since without their ef ciencies, the rms face nancial ruin when a drug is delayed or denied access to the public market, instead of the prospect of the massive revenue stream from a drug that receives FDA approval. According to PhRMA, the pharmaceutical industry spending on infor mation technology was $200 billion in 1993 and $600 billion in 2000. To put these gures into context, IBM, the largest computer company in the world, spent a total of $5 billion in 2000, $4.9 billion in 2001, and $4.8 billion in 2002 on research, development, and engineering. According to a survey by Information Week, pharmaceutical rms spent about 4 percent of their revenue on information technology in 2002. About a quarter of the budget was allotted to hardware purchases and 10 percent to applications (see Figure 4.6). The remainder of the money was spent on salaries and bene ts (35 percent) services (12 percent), research and development (5 percent), and the balance (13 percent) on everything else. By providing their drug development teams with access to the latest biomedical literature in the online databases and the tools to analyze and interpret the ndings, pharmaceutical rms can potentially shave months or even years off the drug development timeline. For example, in 2000 Lilly invested $2 million in developing a digital library to link regulatory and scienti c workers with up-to-date information about molecules. The timesavings associated with online databases or libraries fosters an in
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R&D 5%
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Services 12%
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Salaries 35% Hardware 25%
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Everything Else 13%
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percentage of their total budget, 2002. Source: Information Week
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Applications 10%
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FIGURE 4.6 Typical pharmaceutical information technology spending as a
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creased rate of target (potential drug) identi cation, as well as the ability to identify more rapidly a smaller number of high-likelihood targets. In most pharmaceutical rms, not only are the complexity and volume of data associated with clinical trials such that manual manipulation of the data is virtually impossible, but the FDA requires clinical trial studies to be submitted in electronic form. Thus, there is no escaping the use of comput ers in the drug development process. Furthermore, new diagnostic meth ods, such as gene chips, are inherently linked to computational methods, so that manual methods of data capture and analysis aren t feasible.
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The challenge in assessing biotech computing as a separate market is in dis tinguishing it from the overall computing industry. One reason for the dif culty in identifying the segment of the computing industry that is most closely associated with biotech is that the general industry trends of com puting and biotech are highly correlated. For example, as shown in Figure 4.7, the performance of the biotech and computer industry stocks from
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