A Revolution in R&D

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14 and generally helping to optimize screening. The power of this approach is expected to increase dramatically with the availability of larger data sets for refining the predictive algorithms. (At the moment, however, in silico chemistry has one notable shortcoming: it looks as if it will be suitable for only about 30 percent of targets—the rest fail to yield the requisite structural information—and even then might prove difficult to apply until lead optimization. Our savings are calculated for those targets where in silico technology can be applied.) The potential savings are on average about $130 million and nearly one year per drug. That would add about $90 million in value per drug. For this step of the value chain, then, productivity would double, assuming the same level of investment. As a beneficiary of these advances, a good case in point is Vertex. Starting from an IDD (in silico drug design) platform in chemistry, the company has gone on to develop an integrated value chain in its own right. In silico models have allowed more efficient design of small-molecule drugs than a purely traditional approach, and the company’s discovery focus has been on certain target classes that benefit most from proprietary in silico technologies. This approach has met with considerable success, culminating in one of the biggest biotech alliances so far (with Novartis, and worth $813 million). Vertex can fairly claim to have the strongest small-molecule drug pipeline within the biotech industry. With one drug on the market and twelve candidates in development, it compares favorably with some of the big pharmaceutical pipelines. Serious money can be saved for the target classes where in silico chemistry works. —Director of chemistry, major pharmaceutical company Development Three key genomics advances look set to increase capacity here. In silico ADME/tox (absorption, distribution, metabolism, and excretion/toxicity) and high-throughput in vitro toxicology are revolutionizing the preclinical phase through their power to predict drug properties. And surrogate markers (physiological markers that correlate with elements of drug response), applied in both preclinical and clinical trials, evaluate drug effects more efficiently than before: they are quick to identify failing compounds, and once regulatory approval is granted, will be used to identify passing compounds too. In combination, the potential savings available in the short term are on the order of $20 million and 0.3 years per drug. That would add about $15 million in value per drug. But these approaches will become even more valuable as clinical data on the relationship between genes, gene expression, and disease accumulate and regulatory agencies begin to accept clinical-marker data: the potential savings could rise to $70 million. These technologies are being adopted by forwardlooking chemistry companies, and are enabling them to pull certain preclinical activities into the chemistry part of the value chain. For example, ArQule has recently acquired Camitro to incorporate an integrated in vitro and in silico ADME/tox platform into its own set of capabilities. These are not the only advances likely to transform productivity during the development phase. Pharmacogenomics—through its power to identify subgroups of patients who respond differently to a drug under study—offers the promise of streamlining clinical trials; we explore this topic in more detail later. Beyond genomics (and beyond the scope of the current report), “e-technologies,” such as electronic patient recruitment and monitoring via the Internet, are expected to speed up the launch and completion of clinical trials. Beyond the Traditional Value Chain: Chemical Genomics The various productivity gains just outlined occur within specific steps of the value chain. But suppose you could transcend the traditional value chain, or refashion it to streamline R&D. That is one of the revolutionary prospects now opening up. The key is chemical genomics, and the way it will dissolve the old boundaries is by introducing into the value chain a kind of parallel processing. (See sidebar, “Chemical Genomics—Forward or Reverse.”)
CHEMICAL GENOMICS—FORWARD OR REVERSE When companies say they are pursuing chemical genomics, they are usually referring to large-scale reverse chemical genetics. (That is how the term is used in our report.) This approach involves finding chemical compounds that bind to a known target. Companies often perform this task for entire target classes; it is especially popular for protein classes that are known to be highly drugable, such as Gprotein coupled receptors (GPCRs). The assay for binding does not need to provide functional information relevant to a specific disease state—biological function can be assessed in validation experiments. The alternative is forward chemical genetics. This approach begins with functional knowledge. A library of compounds is screened in an assay that tests for changes in a specific biological function. One immediate result would be to process the glut of identified targets more quickly: instead of joining the logjam at the validation stage, a great many of them can now be diverted directly to screening. If they fail there, they can be discarded right away, and thus simply bypass most of the validation stage altogether. In other words, screening moves up the value chain to rest alongside validation, in a parallel rather than consecutive position. By bracketing the industrialized steps of target identification and chemical screening, chemical genomics has given the value chain a remarkable makeover. The key is to move lengthy, messy biology far downstream where you know it’s worth pursuing. Many targets aren’t drugable, so just validate the smaller drugable subset. —SVP of discovery, leading biotech company The effect of this new value chain is dramatic: time to drug is cut by a further two years (that’s on top of the year already saved by using genomic target identification). On the other hand, there is a large 3. In July 2001, Aurora Biosciences was acquired by Vertex Pharmaceuticals. The intention is to screen a library against all expressed genes in the system under investigation. This approach has the tremendous advantage of allowing the identification of targets without any presumptions as to their function. Additionally, these targets can help to elucidate the mechanism of disease, thereby revealing other potential targets in relevant pathways. The drawback is that forward chemical genetics has not yet been industrialized, and throughput levels are therefore very low. According to our model, implementing it today would increase costs to more than $1 billion per drug, owing to the use of “slow” biology, which is needed to set up the screening assays in chemistry. The expert estimate is that forward chemical genetics is still as much as five years away from being economically feasible. increase in cost, offsetting all cost savings from target identification. But the tradeoff is still positive. In a highly competitive market, where new entrants are continuously eroding share, chemical genomics can add more than $200 million in value per drug. (In less competitive conditions, the value added may be as little as $20 million.) No doubt chemical genomics costs more—but you take the loss to gain the speed. Time is money. —SVP of discovery and technology, major pharmaceutical company One important drawback of chemical genomics is this: it is limited mainly to known target classes. With targets of unknown function, results become very difficult to interpret. The proxy assays used for screening—heat-stability assays, for instance—tend to yield both false positives and false negatives. Nevertheless, chemical genomics is already being pursued throughout the industry. Several big pharmaceutical companies have adopted it, and genomics companies such as Aurora Biosciences 3 and 15
Page 1 and 2: A Revolution in R&D HOW GENOMICS AN
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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 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

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