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Infrastructures, by their very nature, lag behind the technologies they sup port. Only a stable infrastructure facilitates the introduction of new
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processes and technologies. However, at some point, the infrastructure must evolve to allow next-generation technologies to be applied to new chal lenges. Normally, this evolution is a slow process that involves standards committees with representatives from academia, industry, and the govern ment. However, at times, the conditions demand an immediate change to avert a catastrophic failure, loss of an industry, or even loss of life. When the Severe Acute Respiratory Syndrome (SARS) rst appeared in China in November 2002, the infrastructure for sharing information with the worldwide medical community was inadequate. However, as the deaths and the costs of travel restrictions rose, the world community transformed the communication infrastructure so that the people and processes sup ported open, real-time sharing of data. In record time, the virus responsible for SARS was sequenced, and scientists in China, the United States, Canada, and elsewhere began working on a vaccine (and racing to acquire patent protection for their work). In addition to the work of scientists, the infrastructure supported the public s need to know. Statistics of reported probable cases were available to the public through the WHO s Web site as soon as the data were available. Although SARS is an unfortunate reason to upgrade the worldwide medical communications infrastructure, the result is a the evolution of a system people and processes better able to handle a biological emer gency with the application of existing technologies.
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When you come to the fork in the road, take it. Yogi Berra, author and baseball player
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n the decades since the discovery of the double helix, great strides have been made in the science of biotech. Our understanding of biology has been compacted from myriad observations of nature to a series of molecu lar interactions. In addition, scientists and engineers have developed hun dreds of technologies and tools to identify, manipulate, and transform genes. With the sequencing of the human genome complete, laboratories throughout the world are retooling in preparation for the next stage of sci enti c research, that of relating genomics and proteomics to human health. As most of the scienti c advances over the past century have demonstrated, if it can be imagined, it can be realized. Given suf cient time and funding for basic and applied research, these challenging scien ti c goals will be achievable. Aside from scienti c gains, the decades of research in traditional phar maceuticals that resulted in phenomenal gains for investors suggest that the biotech industry has a vast, largely untapped potential for revenue genera tion. As introduced in previous chapters, realizing this potential in a biotech company is a function of the people, business and legal processes, as well as the nancial and technical resources available. An experienced and credible management team, a viable technology portfolio, and a proven mechanism of value capture are all fundamental ingredients for success. The most common value capture mechanisms in biotech are linked to traditional pharmaceuticals, tools, and service business models. The tradi tional pharmaceutical business model is based on the sale of a single, very high margin blockbuster drug that can generate enough revenue in a few years to pay for a decade or more of research and development on perhaps dozens of drug candidates, while also creating a handsome pro t for the pharmaceutical company and its shareholders. The tools model involves
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creating marginally different products, such as computer-based drug dis covery aids, that can easily scale. However, because of the limited potential of the tools model to generate signi cant revenue, many of the tool compa nies in biotech have shifted to the pharmaceutical model. Many diagnostic laboratories that offer genomic testing follow a service model, which has the advantage of short development time, but is typically slow to scale. Although it hasn t been demonstrated which business model will ulti mately be the most successful in biotech, it s clear that continued advances in the eld of biotech hinge on a continuous stream of nancing from a va riety of sources. This chapter explores nancing in biotech, including the global realities of the post-2000 market. It reviews the stakeholders in the primary and secondary biotech industries, and examines the signi cance of nancing from public investors, industry, government, academia, and ven ture capitalists.
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