A Revolution in R&D

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42 POTENTIAL SAVINGS—FROM THEORETICAL TO PRACTICAL In the first two chapters of this report, we assessed the potential savings for the two waves of the genomics revolution: first, the substantial savings attainable through genomics technologies; second, the greater but far less certain savings attainable through genetics approaches, notably disease genetics and pharmacogenetics. Those assessments show the high end of the achievable range, and they view the two waves singly rather than jointly; that is, they indicate discrete and best-case scenarios, which will be difficult for companies to realize in practice and impossible to combine. A more integrated assessment needs to average out the achievable range—to take account of worst-case scenarios too. And it has to analyze the various likely combinations of approaches from the two waves, rather than treating genomics and genetics approaches in isolation. According to the combination selected, the R&D value chain as a whole will assume a particular new form and favor a particular subset of potential drugs. That is a crucial consideration for a company engaged in building a portfolio of technologies: the more com- DRUG R&D VALUE CHAIN ACTIVITIES Biology Target ID Target Optimization Validation Genomic target discovery Chemical genomics target discovery Genetic target discovery with pathway analysis Chemistry Screening Optimization In silico Traditional binations of approaches, the greater the company’s versatility in pursuing different drug subsets. Drawing once again on our economic model, we have examined the impact of each feasible genomics-based approach to target discovery, augmented by pharmacogenetics and genomics technology whenever possible. And we have estimated the realizable value in each case: first, by calculating the cost, time, and added value for each possible combination of approaches, and then—adjusting for the percentage of targets each approach is able to process—by calculating a weighted average per drug. The result is three broad scenarios: • A genomics-based approach: industrialized target identification, supplemented where applicable by downstream genomics technologies (in silico chemistry, in silico toxicology, in vitro ADME, surrogate markers, and pharmacogenetics) • Chemical genomics: industrialized target identification, followed by chemistry and traditional validation conducted in parallel, and supplemented where possible by the downstream technologies just listed 30% 70% Development Preclinical Clinical Genomics: In silico/in vitro tests, surrogate markers Pharmacogenetics Genomics and pharmacogenetics Traditional 73%
• Disease genetics supported by pathway analysis, combined where possible with the various downstream technologies The chart on page 42 shows the three scenarios, and the weighting applied to calculate the average cost per drug. Here, by way of example, are realistic savings estimates generated by our economic model for these three approaches. First, a genomics-based approach in a traditional value chain structure. This provides an ideal foundation for a portfolio perspective on drug discovery. It applies to both known and unknown target classes, and offers impressive potential savings of $200 million and 1.5 years per drug. Next, chemical genomics is probably the most competitive approach for targets where there is an established high-throughput chemical screening assay. The time savings are particularly important—nearly 3.5 years—boosting drug revenues by means of intellectual property rights and first-to-market advantages. When combined with other genomics approaches, chemical genomics offers potential savings of about $100 million per drug. The approach lends itself particularly well to certain targets, such as GPCRs, so a company electing not to pursue chemical genomics would be at a disadvantage if it retained such targets on its wish list. Finally, disease genetics supported by pathway analysis is, theoretically, the most direct route to understanding human disease. This approach is applicable to known and unknown target classes and to targets overlooked in animal-based studies. And on the face of it, it is the most attractive approach financially, with cost savings of $400 million. There is an extended time to drug, however, amounting to about one year, though that drawback would often be offset by the early securing of intellectual property rights. Unfortunately, this inviting approach is still not affordable, and in fact its scientific feasibility remains unproven. (See the graphs below for a summary of expected savings.) REALIZABLE RESULTS BASED ON DISCOVERY APPROACH Weighted average of approaches across value chain Cost to drug Pre-genomics Genomics-based approach Chemical genomicsbased approach Genetics-based approach 1 Time to drug Pre-genomics Genomics-based approach Chemical genomicsbased approach Genetics-based approach 1 0 0 200 5 400 10 475 600 11.3 675 800 13.3 15 780 14.7 880 Cost ($M) 15.8 1,000 20 Time (years) 1With pathway analysis, 50% through genetic simple systems, 50% through genomics expression profiling. Assumes resolution of scientific and technological questions. 43
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 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

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