A Revolution in R&D

More documents

Recommendations

Info

12 The Opportunities What is the impact of genomics on the economics of R&D? To what extent will genomics improve productivity overall, and what will its effects be when applied at various points of the value chain? What other incidental advantages might genomics bring in its wake? These crucial questions have received a great deal of attention of late, and a wide variety of responses. To address the questions in a rigorous, fact-based way, we built an economic model of the entire R&D value chain, grounded in a program of discussions within the industry (more than 100 meetings with more than 60 scientists and executives from nearly 50 companies and academic institutions.) (See the methodology section at the end of this report.) Realizing Savings Before genomics technology, developing a new drug has cost companies on average $880 million, and has taken about 15 years from start to finish, that is, from target identification 2 through regulatory approval. (See Exhibit 2.) Of this cost, about 75 percent can be attributed to failures along the way. By applying genomics technology, companies could on average realize savings of nearly $300 million and two years per drug, largely as a result of efficiency gains. That represents a 35 percent cost and 15 percent time savings. (And those are the savings possible with technologies that are available today; when new or improved genomics technologies emerge, the savings will be even greater.) If companies wish to stay competitive, they have no choice: they must implement genomics technologies. (See Exhibit 3.) Doing so, however, will hardly produce such huge savings immediately, or automatically. It will take a few years, and many deft decisions, for the savings to be realized. The early years of implementation may in fact involve an increase in costs as the learning curve is negotiated for novel targets—specifi- EXHIBIT 2 DRUG R&D IS EXPENSIVE AND TIME-CONSUMING Cost: $880 million total Approximate cost ($M) 165 205 Time: 14.7 years total Approximate time (yrs) 1 Biology 2 Target ID Target Validation 40 0.4 Chemistry cally, as the necessary quality controls are established—and as major strategic decisions (about personnel and processes, for instance) are confirmed or revised. More on these challenges later. But first, we will take a closer look at the long-term upside, detailing the savings at various steps along the value chain. 120 2.7 Screening Optimization 90 1.6 Development 260 7 Preclinical Clinical SOURCES: BCG analysis; industry interviews; scientific literature; public financial data; Lehman Brothers; PAREXEL’S Pharmaceutical R&D Statistical Sourcebook 2000. NOTE: Cost to drug includes failures. Target identification includes initial experiments that companies may have outsourced to academic research institutions. 2. Includes initial experiments to identify potential targets. Traditionally, companies have sourced much of this research from academia.
EXHIBIT 3 GENOMICS CAN YIELD SIGNIFICANT SAVINGS Cost to drug Pre-genomics Post-genomics target ID Plus in silico chemistry Plus preclinical and clinical advances 1 Time to drug Pre-genomics Post-genomics target ID Plus in silico chemistry Plus preclinical and clinical advances 1 ID Biology Target ID Target Validation 0 0 200 Cost ($M) Target Discovery/Biology The identification of targets is being industrialized—through the use of technology such as gene chips to perform gene expression analysis, for example—and then further enhanced by bioinformatics. Scientists can now use a single gene chip to compare the expression of thousands of genes, in diseased and healthy tissue alike, all at once, and can then use informatics technology to find follow- 5 Chemistry 400 Screening Optimization 600 10 610 590 800 740 13.0 12.7 880 13.8 15 Time (years) Development Preclinical Clinical SOURCES: BCG analysis; industry interviews; scientific literature; public financial data; Lehman Brothers; PAREXEL’S Pharmaceutical R&D Statistical Sourcebook 2000. 1,000 14.7 1Includes surrogate marker savings from early elimination of unpromising candidates, not from early FDA approval; does not include potential savings from pharmacogenetics. up information, on these or related genes, in databases around the world. (Target validation, however, seems difficult to industrialize, owing to the “slow” biology of whole-animal systems still involved, and is not yet showing significant productivity gains.) In all, the potential savings per drug are on average about $140 million and just under one year of time to market, achieved entirely through improved efficiency. That would add about $100 million in value per drug (assuming an “average” drug with peak annual sales of $500 million). So for this step in the value chain, productivity would increase vastly: it would be six times as high as before, assuming the same level of investment. A sixfold increase in the number of potential targets! Several companies have already benefited handsomely from this windfall. Take the case of Millennium, which was an early adopter of industrialized biology. The company, anticipating an overabundance of targets, established a business model in which it sells off much its output and uses that income to fund internal research. Starting from its early genomics platform, Millennium has strategically acquired or partnered with other platform companies to establish an integrated drug discovery value chain. From the other perspective, pharmaceutical companies such as Bayer and Aventis have made deals with Millennium, in the expectation of profiting from the new abundance of targets they can choose to pursue. Lead Discovery/Chemistry Chemistry is being revolutionized by in silico (that is, computer-aided) technology—specifically, virtual screening supported by chemoinformatics. In virtual screening, potential lead chemicals are assessed with computer algorithms to test how likely they are to interact with a target. Chemoinformatics provides the necessary platform for virtual screening, using data and analysis from high-throughput screening (HTS) and other chemistry activities. This approach increases efficiency by focusing compound synthesis, reducing the number of assays, increasing the parallelization of screening steps, 13
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 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 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

A Revolution in R&D

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?