A Revolution in R&D

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22 UPSTART START-UPS—THE COMPETITORS CLASSIFIED As the e-commerce revolution has demonstrated, disruptive technologies tend to spawn start-ups that aim to exploit the disruption, either as suppliers to, or as replacements for, incumbents disoriented by a changing world. In the case of genomics, the incumbents are easy to identify: the traditional pharmaceutical companies and the larger fully integrated biotech companies. But who are the start-ups? Genomics companies can be classified in three broad groups, on the basis of how closely or distantly they are related to the actual developing and marketing of drugs. The group furthest away contains the companies that supply enabling technologies, in the form of hardware or software. These companies resemble the merchants of the California gold rush who sold pickaxes to the miners, or more recently, companies such as Cisco and Sun Microsystems that have been providing the necessary infrastructure for the multitude of e-commerce practitioners. Examples of such companies are PE Biosystems, the supplier of highthroughput sequencing machines, and Affymetrix, the preeminent gene-chip manufacturer and supplier. The second broad group contains the companies supplying information and knowledge, including those companies that generate proprietary databases and offer access to them through subscriptions or fee-per-use business models. One of the best-known examples is Celera, which sells subscription-based access to human and animal model-sequence data. The group also includes companies that are attempting to integrate and exploit those databases to conduct in silico R&D. An example is LION Bioscience, which integrates information from public and private sources into a single platform to make targets and leads easier to identify and analyze. Finally, there is the group of companies that develop and sell more traditional “physical” drug intermediates—targets and lead compounds. We call these platform and orchestrator companies. Platform companies deploy proprietary technology in the quest for promising targets and leads. One such company is Aurora Biosciences, which has developed proprietary high-throughput screening technology to exploit an opportunity in screening and chemical genomics. Another example is MorphoSys, which has developed a platform for rapid development of high-affinity antibodies, for use in target validation and therapeutic antibody discovery. Going one step further are orchestrator companies, which string together adjacent platforms to create optimized segments of the R&D value chain. As the orchestrators extend their value chain, they can sell drug candidates that have progressed further and further downstream—and have thus become more and more valuable. Although these companies are still selling only intermediates, they show every sign of graduating into fully integrated drug companies. Millennium has already made that transition: concentrating initially on genomics target discovery, it has subsequently developed a full R&D pipeline in its own right. What is the outlook of each of these groups? The first two (the pure suppliers, either of enabling technologies or of information and knowledge) would appear to be well positioned if they target areas of scarcity (that is, bottlenecks) with proprietary prod-
ucts, or if they have enough clients to achieve scale efficiencies reachable only by supplying multiple companies. But so far, most of these companies have struggled to find a sustainable, profitable business model. Meanwhile, the third group seems in the most promising position. The pharmaceutical business remains attractive, with margins averaging more than 80 percent, so it is easy to see why so many genomics companies aspire to become drug companies. But • Where is industrywide scale to be found rather than just company-level scale? Which capabilities should we therefore develop in-house, and which through partners? • How will any new technologies affect the rest of the value chain? How can we optimize decision making and information flow up and down the value chain? •What are the implications for the organization of the changes we wish to make? How feasible is the necessary restructuring? And what would be the most efficient way to carry it out? there are many hurdles en route, and overambitious companies risk tripping over them. Although many of these companies may fail, those that succeed will have a transformational impact on the industry. Moreover, the traditional drug companies seem to be moving in the opposite direction, increasingly outsourcing portions of their R&D value chains. What is going on? We will try to answer that question in the third chapter, when we examine these trends in more detail. These questions can be addressed by thorough, thoughtful analysis. Key investment decisions will be required, as well as a carefully planned implementation program to ensure that the value of those decisions is captured. In the next chapter, we turn to genetics and analyze its likely impact on R&D productivity. In the final chapter, we will examine more closely the strategic choices and operational implications of the various changes in prospect. 23
Page 1 and 2: A Revolution in R&D HOW GENOMICS AN
Page 3 and 4: A Revolution in R&D HOW GENOMICS AN
Page 5 and 6: Table of Contents ABOUT THE AUTHORS
Page 7 and 8: Foreword To meet growth targets, ph
Page 9 and 10: cogenetics). The productivity gains
Page 11: Introduction Throughout the pharmac
Page 14 and 15: 12 The Opportunities What is the im
Page 16 and 17: 14 and generally helping to optimiz
Page 18 and 19: 16 Cellomics are well positioned to
Page 20 and 21: 18 trials, if the company were able
Page 22 and 23: 20 almost to that of more familiar
Page 26 and 27: 24 Chapter 2: The Impact of Genetic
Page 28 and 29: 26 genetics-based form of pharmacog
Page 30 and 31: 28 DISEASE GENETICS—VARIOUS APPRO
Page 32 and 33: 30 Efficiency improvements in targe
Page 34 and 35: 32 duce targets high in quality but
Page 36 and 37: 34 would be excluded from the trial
Page 38 and 39: 36 Given these technological limita
Page 40 and 41: 38 Side-effect-based pharmacogeneti
Page 42 and 43: 40 moves, along with the relative s
Page 44 and 45: 42 POTENTIAL SAVINGS—FROM THEORET
Page 46 and 47: 44 ogy—to discover new drugs. Bro
Page 48 and 49: 46 INDUSTRY CHANGES The genomics la
Page 50 and 51: 48 Selecting Technologies According
Page 52 and 53: 50 tions, interfaces, and so on. Th
Page 54 and 55: 52 CASE STUDY: REDISCOVERING DISCOV
Page 56 and 57: 54 other—often literally—on col
Page 58 and 59: 56 The formidable operational and o
Page 61 and 62: Methodology The many diverse studie
Page 63 and 64: The Boston Consulting Group publish

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