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Apr 15, 2007 (Vol. 27, No. 8)

Searching for Viable Biobusiness Models

The Reality of the Industry's Nature Must Be Considered If It Is Ever to Become Successful

  • For much of its 30-year history, biotechnology has been a sector in search of a business model. In its primordial days of the mid and late 1970s, the model of choice and perhaps necessity was the contract R&D house. By the early 1980s, after Genentech’s wildly successful IPO, every new entrant aspired to become a fully integrated pharmaceutical company (FIPCO).

    Investors soon learned the pitfalls of this approach: the FIPCO model works great when a company defys the odds and succeeds in getting its first drug approved, as was the case for Genentech, Amgen, and Genzyme; it is a completely different story when the drug fails, as was the case for companies like Cetus.

    By the early 1990s, FIPCO was out, and the alliance-driven firm was in. Under this model, the biotech company would specialize in proprietary, early-stage R&D and then license drug candidates out to selected partners during the clinical development phase in exchange for upfront fees, milestone payments, and royalties on future product sales.

    Then, the explosion of technologies, such as high-throughput screening, combinatorial chemistry, and genomics, in the mid 1990s ushered in the era of the platform model. According to this scheme, firms sought to earn returns by selling subscriptions or charging licensing fees for broad access to their particular technology. This was based on the idea that it is better to get a 1% cut on the returns across many firms and products than to get 100% of the returns as well as all the risks of trying to develop your own drugs.

    Alas, with the bursting of the genomics bubble in 2001, however, the platform model was tarnished. Once again, investors became interested in companies with products that could either be licensed or developed or that made potentially juicy acquisition targets. In essence, we have gone full circle.

  • Merging Science and Business

    The churn in biotech business models is not completely surprising nor is it necessarily a bad thing.

    The biotech sector is, in many ways, an experiment in the fusing of science and business. With the exception of some unusual organizations, like Bell Labs, these two segments have generally lived in separate worlds. Science was the province of universities and government-sponsored research. Returns were measured in the currency of peer-reviewed journals, impact, and reputation. Business was the responsibility of enterprises with well-defined yardsticks of profitability and returns to shareholders.

    Going right back to the founding of Genentech, the challenge lies in the melding of science and business: we just do not have much experience with this type of truly science-based business. Biotech was cutting a new swath through the economic landscape. There were no proven business models. Thus entrepreneurs, investors, and managers had no choice but to experiment. In essence, this experiment is still going on today.

    By definition, experimentation is going to involve failure. It was natural to try to emulate other sectors—like semiconductors and even software. The biotech industry, however, is really quite different. Firms are often working right at the edge of scientific knowledge—they are not just consumers of science, they are producers of it as well.

    What was learned from this failure is that models that worked so well in other contexts were often not a good fit for the realities of biotech. Biotech is still very much an experiment in process, but we can glean some lessons after 30 years.

  • Realities of Biotech

    The success of any business model depends on how well it fits with the realities of the environment. In a science-based business like biotech, the nature of the science creates certain challenges and constraints. An appropriate business model will address those specific problems.

    However, most methods have been out of tune with the realities of the science. Indeed, I would go so far as to say that many business models have been predicated on incorrect assumptions about how the science of biotech will change the drug R&D process. Let me highlight three of these.

    The first is that the science of biotech will lead to dramatically shorter time lines for drug R&D and a more rational, less risky process. We have all seen business plans or heard about companies who claim that their technology or their approach to drug R&D is so superior that they will cut the time between discovery and approval down to five years instead of twelve and have a much higher rate of success.

    While this is likely to be true over the long term, it has certainly not been true over the past 30 years. Attrition rates of drugs in development have actually increased as firms have taken on novel targets. The complexities of human biology means that drug R&D is still a long-term and highly uncertain process.

    Thus, instead of crafting business models designed to address the need for managing risk over long periods of time and providing appropriate funding arrangements, we often see business styles that would work great if in fact the drug R&D process was much shorter and much less uncertain.

    The second problematic hypothesis has to do with the nature of the knowledge base. Business models are often predicated on the theory that a specific new technology represents the “golden key” to the drug discovery puzzle. In the early 1980s, it was rDNA and mAbs, then it was structure-based drug design, and then tools like combinatorial chemistry, high-throughput screening, and genomics took center stage; more recently, RNAi has taken the spotlight.

    Entrepreneurial firms are often started based on the scientific founders’ particular expertise in one of these technologies. The problem is that drug R&D is not a one-star show. Instead, it requires the integration of many different tools and technologies. Business models that put an emphasis on a particular tool or technology being the focus fail to provide the integration needed to fully exploit the evolving knowledge base.

  • Old and New Co-Existing

    Finally, since its dawn, biotech business models have been forecast on an over-simplified notion that the new technology makes the old technology obsolete. This is, after all, what happens in industries like semiconductors—where, for instance, solid state transistors made vacuum tubes obsolete—and other high-tech settings. This assumption is one of the reasons that start-up activity has always been so high.

    With every major wave of new science, from rDNA and mAb onward, we have seen a wave of new entrants that specialized in new tools. The viability of these firms is predicated on an assumption that their particular new technologies are superior to the existing methods of other firms.

    The reality in biotech is more complicated. Old and new not only co-exist, they often complement one another. Thus, not only do we need integration across new technologies, we need it between the old and new ones as well.

    If we look at the realities of the science of biotech, it can help us understand the key characteristics of appropriate business models. The models need to be able to deal with the problems of managing risk over long periods of time and to be able to foster integration across a broad array of disciplines and knowledge bases, as well as between older and newer technologies.

    At present, the business models of biotech have worked poorly because they were based on the wrong inferences about the science. To date, the sector has defied economic gravity by continuing to attract capital despite losses and disappointing performance. Getting the sector healthy will require continued experimentation with business models that address the true underlying realities of the science of the business.

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