A Revolution in R&D

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50 tions, interfaces, and so on. The necessary work can be divided into three broad areas: •Rebalancing the value chain • Establishing the new organization and its governance • Managing organizational change Rebalancing the Value Chain The old ways of conducting R&D are often unsuited to the new era. As the first chapter showed, the traditional R&D value chain no longer works. For one thing, its smooth flow quickly becomes disrupted by a series of bottlenecks, induced by the different productivity gains at different phases. For another, its sequence is unsustainable, since the new technologies dance to a different schedule: much of the chemistry phase might now take place simultaneously with target validation, for example. To reinstate a smooth flow (while enjoying the new, much accelerated rate of throughput), R&D needs to ease the bottlenecks and adjust to a reconfigured value chain. And that means redistributing resources and, more importantly, redesigning processes, as well as keeping the new value chain in balance. Restoring Balance: Reallocation versus Redesign At first sight, the bottleneck problem would seem fairly simple to resolve: scale up downstream steps to meet the increased demand. But how feasible would that be? The number of targets identified could increase sixfold or more. To scale up to meet that increase, a company accustomed to spending $1 billion on all of R&D would now have to spend more than $1.5 billion on target validation alone. Another simple approach suggests itself: adjust resources along the value chain in order to bring the uneven phases back into balance, shifting funds from more efficient phases (notably target discovery) to less efficient downstream phases (such as preclinical). But such reallocation of resources is, on its own, an overcautious measure, and will not have a really dramatic impact on R&D economics. It neglects, or even distracts from, the central opportunity that genomics offers: the opportunity to “raise the game” by changing fundamentally the way R&D is conducted. This transformation of R&D will derive above all from the bold reconfiguring of processes, for the sake of both physical process flow and information flow. For the former, new technology platforms need to be integrated and optimized, both within value chain steps and across the value chain. (In some cases, this may require a discipline and a rearrangement comparable to the moving assembly line introduced by Henry Ford in 1913.) As for information flow, the tremendous amount of data generated by the new technologies remains worthless unless translated into functional information and supplied punctually—that is, in time to influence the decisions being made. (See sidebar, “Establishing a Unified Informatics Structure.”) The extent of the redesign, and the particular shape the new flows take, depend very much on the company’s strategy choices. Processes that are newly industrialized, but that still follow a traditional R&D sequence, need to be systematized. In some cases, however, the traditional R&D value chain will need to be disrupted. To integrate chemical genomics and genetics, for instance, would necessitate a major restructuring of the value chain. Chemical genomics introduces a new parallelism, as target validation and chemistry activities are conducted simultaneously; the two processes now interact rather than just interface. And genetics introduces feedback loops, where late-phase findings (such as genetic information from the clinic) feed back into earlier steps of the value chain (such as diseasegenetics-based target discovery). In anticipation of any process redesign, individual function heads should be pondering the contingencies: how and when genomics might affect them, and what actions to take when it does. As one bottleneck is relieved, another is created: When will the bottleneck reach their step in the chain, and what will its impact be? What new technologies and
ESTABLISHING A UNIFIED INFORMATICS INFRASTRUCTURE For a company to extract full value from the new technologies and the copious data that will emerge from them—that is, to transform data into knowledge—it will have to devise a comprehensive informatics vision and architecture. The vision will articulate the role of informatics as a potential source of competitive advantage. The architecture will have three essential components First, there must be an optimized information flow across the newly industrialized research and development value chain. Standards will need to be established to ensure that data are formatted, organized, and defined consistently. Hardware and applications will have to be linked and networked appropriately so that information can move where it needs to, feeding subsequent steps in the process. Second, a centralized knowledge management system is required, to capture and store the data, integrate it with external data, and make it available throughout the company. Finally, powerful analytical tools will be required, to mine and make sense of the data—sophisticated algorithms, visualization tools, and so on. To develop and integrate these components, most companies will have to invest heavily. The costs may look particularly high in relation to traditional costs, but that is partly because the industry has generally underinvested in IT. (One large biotech company, following its informatics upgrade, reports a threefold increase in its annual IT budget.) To focus the investment accurately, a coherent plan is once again essential. A critical decision is whether to develop the capabilities in-house, outsource to solutions providers, or purchase and integrate informatics packages. The choice or choices made will depend on such factors as available internal expertise, the amount of integration with legacy information sys- tems required, and the availability of reliable integration vendors, package suppliers, or solutions providers. That last factor may prove particularly difficult to assess. Who would provide the most reliable and suitable assistance? Today, no single provider of application software provides all the functionality needed. The informatics industry is crowded with small start-ups offering niche products; the cumulative market capitalization of all publicly listed bioinformatics companies scarcely amounts to one-eighth of GlaxoSmithKline’s annual R&D budget. And while larger IT solutions providers are gearing up to serve the burgeoning needs of this market, they are still in the process of developing internal life-sciences capabilities. Given that each prospective solutions provider is likely to try to make its offering the centerpiece of the company's informatics architecture, and given that multiple solutions will need to be knitted together, companies would be wise to solicit independent, unbiased advice before deciding on particular vendors. In addition to managing all this informatics complexity, companies will have to deal with a further challenge if they are to implement the new information regime successfully. They will have to find a way to resolve the human-resources and organizational issues that are bound to arise. Talented, experienced informatics personnel are difficult to come by: how to find and keep the right people and how to fit them into the organizational structure are questions that companies will need to address more actively and imaginatively than ever. So too the question of how to change processes and behaviors generally, throughout the organization, to ensure fullest use of the new informatics tools—a question examined in some detail elsewhere in this chapter. 51
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 24 and 25: 22 UPSTART START-UPS—THE COMPETIT
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 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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