Amsterdam Athens Atlanta Auckland Bangkok Berlin Boston Brussels Budapest Buenos Aires Chicago Cologne Copenhagen Dallas Düsseldorf Frankfurt Hamburg Helsinki Hong Kong Istanbul Jakarta Kuala Lumpur Lisbon London Los Angeles Madrid Melbourne Mexico City Milan Monterrey Moscow Mumbai Munich New Delhi New York Oslo Paris San Francisco São Paulo Seoul Shanghai Singapore Stockholm Stuttgart Sydney Tokyo Toronto Vienna Warsaw Washington Zürich
BCG
A Revolution in R&D
H O W G E N O M I C S A N D G E N E T I C S A R E T R A N S F O R M I N G
T H E B I O P H A R M A C E U T I C A L I N D U S T R Y
B C G R E P O RTThe Boston Consulting Groupis a general management consulting firm that is a global leader in business strategy. BCG has helped companies in every major industry and market achieve a competitive advantage by developing and implementing unique strategies. Founded in 1963, the firm now operates 51 offices in 34 countries. For further information, please visit our Web site at www.bcg.com.
The Boston Consulting Group publishes other reports that may be of interest to senior health care executives. Recent examples include:
The Pharmaceutical Industry into Its Second Century: From Serendipity to Strategy
A report by The Boston Consulting Group, January 1999
Ensuring Cost-Effective Access to Innovative Pharmaceuticals: Do Market Interventions Work?
A report by The Boston Consulting Group and Warner-Lambert, April 1999
Patients, Physicians, and the Internet: Myth, Reality, and Implications
A report by The Boston Consulting Group, January 2001
Vital Signs: The Impact of E-Health on Patients and Physicians
A report by The Boston Consulting Group, February 2001
Vital Signs Update: The E-Health Patient Paradox
A BCG Focus by The Boston Consulting Group, May 2001
Vital Signs Update: Doctors Say E-Health Delivers
A BCG Focus by The Boston Consulting Group, September 2001 In addition, BCG’s Health Care practice publishes Opportunities for Action in Health Care, essays on topical issues for senior executives.
www.bcg.com
PETER TOLLMAN
PHILIPPE GUY
JILL ALTSHULER
ALASTAIR FLANAGAN
MICHAEL STEINER
A Revolution in R&D
H O W G E N O M I C S A N D G E N E T I C S A R E T R A N S F O R M I N G
T H E B I O P H A R M A C E U T I C A L I N D U S T R Y
N O V E M B E R 2 0 0 1
© The Boston Consulting Group, Inc. 2001. All rights reserved. For information or permission to reprint, please contact BCG at: E-mail: [email protected]
Fax: 617-973-1339, attention IMC/Permissions
Mail: IMC/Permissions
The Boston Consulting Group, Inc. Exchange Place
Boston, MA 02109 USA
Credits: Left cover photo by Bob Waterston, Washington University, St. Louis, Missouri. Used by permission. The photo shows a bird’s-eye view of one room in the DNA sequencing facility at the Whitehead Institute Center for Genome Research.
Table of Contents
ABOUT THE AUTHORS
4
FOREWORD
5
EXECUTIVE SUMMARY
6
INTRODUCTION
9
CHAPTER 1: THE IMPACT OF GENOMICS
11
Preface 11
The Opportunities 12
The Challenges 18
A Final Word 21
CHAPTER 2: THE IMPACT OF GENETICS
24
Preface 24
Disease Genetics 27
Pharmacogenetics 33
A Final Word 39
CHAPTER 3: MANAGERIAL CHALLENGES
41
Preface: Looking Back and Looking Forward 41
Strategy—Searching for Genomic Competitive Advantage 41
Putting the Strategy into Operation 49
A Final Word 56
CONCLUSION
57
About the Authors
Peter Tollman is a vice president in the Boston office and leads BCG's biopharmaceutical R&D business. Philippe Guy is a senior vice president in the Paris office and leads the worldwide Health Care practice. Jill Altshuler is a manager in the Boston office and a key contributor to BCG’s genomics initiative. Alastair Flanagan is a vice president in the London office and leads the U.K. Health Care practice. Michael Steiner is a senior vice president in the Munich office and leads the German Health Care practice.
Acknowledgments
Sarah Cairns-Smith (Boston) pioneered BCG’s investigation of genomics. Samantha Gray (Boston) has made significant contributions throughout the research and writing phases of the report.
The authors would like to thank the advisory team: Oliver Fetzer (Boston), Hamilton Moses (Washington, D.C.), Niko Vrettos (Düsseldorf), and Craig Wheeler (Boston). The authors would also like to acknowledge the contributions of the project team: Dierk Beyer (Frankfurt), Markus Hildinger (Boston), Raphael Lehrer (Washington D.C.), Nancy Macmillan (Boston), Jonathan Montagu (London), and Joanne Smith-Farrell (Washington, D.C.).
For Further Contact
The authors welcome your questions and comments. For inquiries about this report or BCG’s Health Care practice, please contact:
Alastair Flanagan, London e-mail: [email protected] Philippe Guy, Paris e-mail: [email protected] Mark Lubkeman, Los Angeles e-mail: [email protected] Michael Steiner, Munich e-mail: [email protected] Martin Reeves, Tokyo e-mail: [email protected] Peter Tollman, Boston e-mail: [email protected]
Foreword
To meet growth targets, pharmaceutical companies are going to have to increase R&D productivity. By a for-tunate coincidence, that crisis in expectation is being counterbalanced by a surge of opportunity. Recent years have seen astonishing advances in technology and explosions of data, which are driving two waves of change through the industry—a genomics wave and a genetics wave—and radically reshaping R&D methods and economics in the process. Biopharmaceutical R&D is moving into a new era: almost every link in the value chain has the potential for tremendous boosts in efficiency or success.
But these advances are not assured. Technological hurdles have yet to be overcome, particularly in the genet-ics wave. Moreover, because the productivity boosts are likely to be unequal and uncoordinated, the value chain itself will demand reconfiguring. And so too, in consequence, will many traditional operational pro-cedures and organizational structures. The repercussions of genomics, in other words, are going to reach the furthest recesses of corporate constitution and culture. A true revolution, in short—and one that is already well under way.
BCG has evaluated deeply the economic and business implications of these disruptions. To bolster our inter-nal understanding, we gathered information and perspectives in an extensive program of interviews with leading R&D scientists and executives. Our findings—based on the combination of these interviews, eco-nomic modeling, and client casework—form the substance of this report. Its three sections are devoted respectively to the impact of genomics, the impact of genetics, and some of the strategic and operational implications for biopharmaceutical firms.
The first two sections have already been published separately. They generated considerable publicity, and— more important—considerable comment. We now look forward to your further responses to the report as a whole.
Philippe Guy
In the pharmaceutical industry’s struggle to reach the levels of growth expected of it, one of its key aims will be to increase R&D productivity. And a key means of meeting this challenge is to adopt some of the new technologies and approaches broadly defined as genomics.1 That is bound to be a
com-plicated, perilous, and often painful process, but if companies get their strategy right and overcome the obstacles, they could, in the best case, as much as halve the cost of drug development.
The report is divided into three parts.
The Impact of Genomics
The first great advance of the genomics era is in technology—above all, the integration of new high-throughput techniques with powerful new comput-ing capabilities. The new technologies are active throughout R&D, most immediately at the drug dis-covery stage, and promise to enhance productivity by boosting efficiency.
The staggering investment needed to develop a drug—$880 million and 15 years is the pre-genomics average—could be reduced by as much as $300 million and two years by applying genomics technologies. Productivity gains would be realized at every step in the value chain. Potential obstacles
abound, however. In particular, two broad chal-lenges must be met to realize the savings:
• Target quality must be maintained. Pursuing new target classes could involve unfamiliar costs ini-tially, and these could delay the rewards—though only temporarily. But to jeopardize target quality by withholding that early investment would be to risk higher failure rates downstream, and that would involve far greater costs in the end. • Bottlenecks must be eased. Owing to the
uneven-ness of the efficiency gains at different steps in the value chain, the pipeline’s flow will be impeded at various chokepoints. If the requisite action is taken, an even flow should be restored and the promised rewards should be safeguarded.
The Impact of Genetics
The second great advance of the genomics era is in the quantity and quality of data. From the data, invaluable information about individuals’ genetic variation can be extracted and exploited. In phar-maceutical R&D, genetics will be applied particu-larly to two tasks: identifying genes whose carriers are susceptible to specific diseases (disease genet-ics); and subdividing patients in clinical trials according to variations in drug response
(pharma-Executive Summary
1. Genomics in its narrow sense contrasts with genetics. Roughly, the former concerns itself with the common “standard” genetic makeup, the latter with the distinctive genetic makeup of individuals. But in its broader sense, genomics includes genetics. In this report, the context makes clear which sense is intended.
cogenetics). The productivity gains will be realized mostly in later phases of the value chain, through the boosting of success rates.
This genetics wave is still gathering strength, but in due course could make an even greater impact on R&D than the genomics wave. In an ideal scenario, the savings would exceed half a billion dollars per drug. Several troubling hurdles would have to be negotiated first, however. These include:
• Scientific and technical hurdles. For genetics approaches to work, the disease susceptibility or drug response has to be genetic in nature. The gene in question has to be identifiable and must lead to a drugable target and/or be found in time to streamline trials.
• Economic and market hurdles. The cost of con-ducting genetics studies will need to drop, and the opportunity cost of a restricted label could offset the potential market upside of pharmaco-genetics.
Beyond these hurdles, other challenges will need to be addressed:
• Difficult investment decisions will have to be made, weighing high risk against potentially high rewards. Companies will need to decide exactly
how to participate in genetics—whether to invest in genetics approaches, and how deeply, consis-tent with their level of risk tolerance.
• Unprecedented coordination between marketing and R&D will be necessary. Marketing will need to have a say in deciding which markets and which genetic diseases R&D should concentrate on, and will need to become involved earlier than ever. • Careful attention will need to be given to ethical
considerations. Companies will have to ensure privacy of genetic material, and be prepared to address any concerns the public may have.
Managerial Challenges
With the new wealth of options and the increased interdependencies across the value chain, strategic issues will prove more complex than in the past. Likewise operational issues: many traditional ways of doing business will be disrupted by genomics technologies, and companies may need to restruc-ture fairly drastically.
The range of strategic options available to a company will be dictated by the company’s starting position—its size, beliefs, aspirations, and capabili-ties. Given the magnitude of the opportunities and
the risks involved, momentous investment decisions will need to be made, and at the very highest levels of the organization. And R&D executives will face a daunting new set of management responsibilities and challenges. These include:
• Selecting an appropriate research focus—no longer just the therapeutic area or disease state of interest, but also such dimensions as target class and treatment modality
• Choosing which technologies to implement and when and how to implement them—in-house, or through partnering or licensing
• Rebalancing the value chain—partly by reallocat-ing resources but mainly by redesignreallocat-ing processes and more actively planning and managing capacity • Establishing a unified informatics infrastruc-ture—including a centralized knowledge manage-ment system
• Establishing the new organization—creating new interfaces within the R&D department, between departments, and even between corporations • Revising decision-making procedures—fully
exploiting the latest data in order to select the most promising targets and compounds to move through the pipeline and to optimize their rela-tive resourcing
• Reinforcing these various reforms by engaging the emotional and behavioral issues as keenly as the operational ones
All things considered, companies cannot stand aside. Certainly there are risks in signing up for the revolution, but there is also a great risk in ignoring it—the risk of becoming uncompetitive. The revo-lution is real, and will leave no one untouched.
Introduction
Throughout the pharmaceutical industry, execu-tives are worried. They fear they will not be able to meet the double-digit annual growth expectations implied by high market capitalizations. The requi-site new drugs will not be forthcoming: R&D just cannot deliver them all.
One standard response to this problem is to scale up—that has been the basis of many a recent merger—but while scale can pay off in commercial-ization, global development, marketing, and distri-bution, it is unlikely that scale alone can solve the R&D problem. Another standard response is to buy in drug candidates. Such a Band-Aid approach can-not work indefinitely, and is a risky one anyway, given that the price of these deals will continue to rise as demand for them grows.
The only sure way to address the problem is to increase R&D productivity. And the way to ensure that is either to increase efficiency (lower cost or higher speed) or reduce failure rates along the value chain. Many companies have increased pro-ductivity over the past decade, specifically by reengineering the development phase. That opti-mization may be reaching its limits, however. As for the discovery phase, it has long been less amenable to such improvements. So the problem of
produc-tivity persists. Traditional approaches cannot pro-vide an answer, but genomics can. (See Exhibit 1.) It will not be easy, of course. There are some diffi-cult obstacles en route—diffidiffi-cult, but not insur-mountable. By making informed strategic choices, companies can overcome the obstacles and reap the productivity rewards. Those that embrace the revo-lution most boldly could potentially halve the cost and time it takes to develop a new drug—if they meet certain challenges successfully.
EXHIBIT 1
GENOMICS IMPROVES R&D PRODUCTIVITY
Total cost (time) per step Cost (time) spent Failed targets/ candidates Successful drug Reduce failure Improve efficiency
Total cost to develop a drug
Chapter 1: The Impact of Genomics
Preface
As the science of genomics has advanced, so has the definition. When the term was coined in 1986, it referred mainly to the study of the mammalian genome—specifically, the mapping, sequencing, and analyzing of all its genes. The scope soon expanded, focusing not just on the genes’ structure but on their function as well. More recently, the scope of the term has broadened further, focusing no longer just on knowledge of the genome but also on the exploitation of that knowledge, especially for health care.
Going beyond dictionary definitions, our interest is in what genomics means for the economics of phar-maceutical R&D. On the basis of our extensive research and many discussions with prominent peo-ple throughout the industry, we suggest character-izing genomics, for the purposes of this study, as the confluence of two interdependent trends that are fundamentally changing the way R&D is conducted:
industrialization (creating vastly higher
through-puts, and hence a huge increase in data), and
infor-matics (computerized techniques for managing and
analyzing those data). The surge of data—gener-ated by the former, and processed by the latter—is of a different order from the data yields of the pre-genomics era.
To elaborate. The new high-tech industrialization has increased the efficiency of certain activities beyond recognition. Instead of assigning individual scientists to work manually on modest individual experiments, companies now invoke automation
and parallel processing to conduct experiments much larger in scale and complexity, and at a much faster pace.
Look around this lab—you have to search high and low to find a human heartbeat. Now robots can do the menial things we did in grad school.
—Research leader, leading biotech company
The data that emerge are immensely greater both in quantity and in richness. Enormous databases— detailing gene expression, for example, or homolo-gous genes across species, or protein structures— afford unprecedented comprehensive views of biological processes. Increasingly, researchers can understand properties of the system rather than just individual parts, and that holds out the promise of a more rational approach to drug discovery. The new technology of informatics serves to handle and process all these data. Without it, the data would remain raw material. Informatics was nur-tured by several coinciding factors: the ever-acceler-ating power of computers, refined algorithms, the integration of data and technology platforms, and the versatility of the Internet. The effect is that overwhelming masses of information can now be marshaled, managed, and analyzed as never before. Data are transformed into knowledge.
We could never have achieved drug development that fast with traditional techniques. No way— without the computers we didn’t have a chance.
—VP of chemistry, biotech company
The Opportunities
What is the impact of genomics on the economics of R&D? To what extent will genomics improve pro-ductivity 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 identification2 through
regula-tory 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 effi-ciency 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 compa-nies 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 learn-ing curve is negotiated for novel
targets—specifi-cally, as the necessary quality controls are estab-lished—and as major strategic decisions (about personnel and processes, for instance) are con-firmed 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.
EXHIBIT 2
DRUG R&D IS EXPENSIVE AND TIME-CONSUMING
Cost: $880 million total
Approximate cost ($M) 165 205 40 120 90 260
Time: 14.7 years total
Approximate time (yrs)
1 2 0.4 2.7 1.6 7 Development Preclinical Clinical Chemistry Screening Optimization Biology
Target ID Target Validation
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.
Target Discovery/Biology
The identification of targets is being industrial-ized—through the use of technology such as gene chips to perform gene expression analysis, for example—and then further enhanced by bioinfor-matics. 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-up information, on these or related genes, in data-bases around the world. (Target validation, how-ever, seems difficult to industrialize, owing to the “slow” biology of whole-animal systems still involved, and is not yet showing significant produc-tivity 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 effi-ciency. That would add about $100 million in value per drug (assuming an “average” drug with peak annual sales of $500 million). So for this step in the value chain, productivity would increase vastly: it would be six times as high as before, assuming the same level of investment. A sixfold increase in the number of potential targets!
Several companies have already benefited hand-somely from this windfall. Take the case of Millennium, which was an early adopter of industri-alized biology. The company, anticipating an over-abundance of targets, established a business model in which it sells off much its output and uses that income to fund internal research. Starting from its early genomics platform, Millennium has strategi-cally acquired or partnered with other platform companies to establish an integrated drug discov-ery value chain. From the other perspective, phar-maceutical companies such as Bayer and Aventis have made deals with Millennium, in the expecta-tion of profiting from the new abundance of targets they can choose to pursue.
Lead Discovery/Chemistry
Chemistry is being revolutionized by in silico (that is, computer-aided) technology—specifically, vir-tual screening supported by chemoinformatics. In virtual screening, potential lead chemicals are assessed with computer algorithms to test how likely they are to interact with a target. Chemoinformatics provides the necessary platform for virtual screen-ing, using data and analysis from high-throughput screening (HTS) and other chemistry activities. This approach increases efficiency by focusing com-pound synthesis, reducing the number of assays, increasing the parallelization of screening steps, EXHIBIT 3
GENOMICS CAN YIELD SIGNIFICANT SAVINGS
Cost to drug Cost ($M) Time to drug 880 Pre-genomics 740 Post-genomics target ID 610 Plus in silico chemistry 590
Plus preclinical and clinical advances1 Time (years) Pre-genomics 13.8 Post-genomics target ID 13.0 Plus in silico chemistry 12.7
Plus preclinical and clinical advances1 ID 14.7 1,000 800 600 400 200 0 15 10 5 0 Development Preclinical Clinical Chemistry Screening Optimization Biology
Target ID Target Validation
SOURCES: BCG analysis; industry interviews; scientific literature; public financial data; Lehman Brothers; PAREXEL’S Pharmaceutical R&D
Statistical Sourcebook 2000.
1Includes surrogate marker savings from early elimination of unpromising
candidates, not from early FDA approval; does not include potential savings from pharmacogenetics.
and generally helping to optimize screening. The power of this approach is expected to increase dra-matically with the availability of larger data sets for refining the predictive algorithms. (At the moment, however, in silico chemistry has one notable short-coming: 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 opti-mization. Our savings are calculated for those tar-gets 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 effi-cient 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, culmi-nating 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 devel-opment, it compares favorably with some of the big pharmaceutical pipelines.
Serious money can be saved for the target classes where in silico chemistr y works.
—Director of chemistry, major pharmaceutical company
Development
Three key genomics advances look set to increase capacity here. In silico ADME/tox (absorption, dis-tribution, metabolism, and excretion/toxicity) and high-throughput in vitro toxicology are revolution-izing 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 com-pounds, 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 mil-lion 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 forward-looking 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 incorpo-rate an integincorpo-rated 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. Phar-macogenomics—through its power to identify sub-groups of patients who respond differently to a drug under study—offers the promise of streamlin-ing 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.”)
One immediate result would be to process the glut of identified targets more quickly: instead of join-ing 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 paral-lel 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 down-stream 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
increase in cost, offsetting all cost savings from tar-get 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 phar-maceutical companies have adopted it, and geno-mics companies such as Aurora Biosciences3 and
C H E M I C A L G E N O M I C S — F O R W A R D O R R E V E R S E
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 G-protein coupled receptors (GPCRs). The assay for binding does not need to provide functional informa-tion 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.
The intention is to screen a library against all ex-pressed genes in the system under investigation. This approach has the tremendous advantage of al-lowing the identification of targets without any pre-sumptions as to their function. Additionally, these targets can help to elucidate the mechanism of dis-ease, thereby revealing other potential targets in rel-evant pathways. The drawback is that forward chem-ical 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.
Cellomics are well positioned to exploit the ex-pected resulting demand for screening resources. Aurora is a likely winner in the race to resolve chem-ical genomics-related bottlenecks, since it boasts some of the most advanced screening and assay technologies in the industry. It has an unusual busi-ness model, in that it provides tools and discovery services but does not engage in any drug discovery of its own.
* * *
So much for the imminent efficiency savings across the R&D value chain. They are hardly the end of
the story, of course. Other technological advances are bound to improve R&D productivity further in due course. Important emerging technologies include proteomics, partial target inhibition, and structural biology. (See sidebar, “Technologies in Waiting.”)
Improving Decision Making
The economics of R&D hinge on success rates, and success rates depend largely on a cascade of deci-sions that have to be made again and again: whether or not to pursue a target or lead, and if so, how—to what extent and with what approach.
In this report we have focused on the technologies and approaches that are having the greatest impact on R&D economics today. Several other exciting ad-vances appear likely to make a comparable impact beyond the next three to five years (too far ahead for inclusion in our analysis for this report), in particu-lar, the use of proteomics in target identification,
conditional gene inhibition in target validation, and industrialized structural biology in screening and
drug design.
Proteomics is the study of protein expression and
protein-protein interactions. Its aim is an understand-ing, and ultimately exploitation, of protein function. Identifying proteins through sequence or structure homology has recently become much more efficient, thanks to bioinformatics’ role in analyzing large-scale experiments. One example of a genomics com-pany applying proteomics is Oxford Glycosciences, which is engaged in identifying targets and surrogate markers, both in collaboration with pharmaceutical companies and in an independent pipeline. But pro-teomics is not really industrialized yet, and has high hurdles to overcome before it is.
We examined the economics of proteomic expression studies using two-dimensional gel analysis, followed
by identification of interesting proteins through mass spectrometry.
Under optimal conditions today, this approach has the potential to save about as much in cost as genomics-based approaches do, though not as much in time (about six months less). As the technology becomes industrialized, proteomics could well sur-pass genomics-based approaches, but that is still several years away.
The aim of the second promising technology we investigated, conditional gene inhibition, is to over-come a common problem in target validation. Here is the background. A standard technique for target validation uses “target knockouts.” The potential tar-get is removed, or “knocked out,” from an animal at conception; this results in the total inhibition of the target’s function from embryo to adult. The trouble is that drugs work differently. Very seldom do they inhibit target function fully, and they are taken only after genes have already fulfilled their developmental role in utero. So the use of target knockouts as a tar-get validation technique does run the risk of creating false negatives (in some cases indicated by death, because of the unnatural disruption of embryonic development). What is needed instead of total gene
T E C H N O L O G I E S I N W A I T I N G — O T H E R T E C H N O L O G I E S E X A M I N E D ,
B U T O M I T T E D F R O M O U R R E P O R T
Genomics may offer an opportunity for companies to make the correct decision more often than before. For one thing, genomics can ultimately pro-vide more, better, and earlier information, and good information translates ultimately into high success rates. For another, the implementation of genomics approaches will force companies to rethink their internal decision-making processes. Genomics-based information, together with the ability to mine it productively, gives a company an enormous advantage. Such a company will now be able to make and execute decisions on targets and
leads with greater speed and consistency than before. Guided by more rigorous selection criteria, the company should go on to improve its success rates and hence its productivity.
A mere 10 percent improvement in accuracy of decisions at any stage would confer disproportion-ately large benefits. Consider, for example, all the target/lead pairs that fail just before clinical trials: if a company were able to decide in just one out of ten such cases against pursuing the target in the first place, it would save as much as $100 million per drug on average. As for INDs that fail clinical
inhibition, therefore, is conditional gene inhibition, which mimics the partial inhibitory effect of a drug. Several promising approaches have emerged, includ-ing forward genetics, chemical genomics, and molecular switches that modulate gene expression, but their practicality has still to be proved.
Examples abound of genomics companies engaged in developing these target-validation techniques. Lexi-con Genetics, Exelixis, and Ingenium, for instance, are using mass mutagenesis on animals such as mice and zebrafish. In a more focused project, Hypnion is using forward genetics and other approaches to understand sleep-wake disorders in mammals. What benefits lie in wait? By eliminating the false negatives associated with the current knockout tech-nique, these new technologies could double or even triple the number of validated targets, and in that way save up to $200 million per drug. At the moment, however, these new kinds of validation (with the exception of certain chemical genomics approaches, discussed in the main text) are still mainly limited to “slow” biology.
Finally, structural biology is used for generating and analyzing the three-dimensional structure of targets
for virtual screening, and is essential to in silico drug design. Unfortunately, it currently entails protein crystallization (to prepare the proteins for visualiza-tion by X-ray diffracvisualiza-tion), which is a difficult, labor-intensive manual process. Speedier alternatives, such as NMR spectroscopy, cannot predict overall protein shape adequately, being restricted to protein subsegments. As a result, in silico modeling remains limited in its applicability: the algorithms cannot boast really high precision for target classes where no example structures are available.
Several projects, both public and private, are under way to upgrade structural biology platforms to the point where they will achieve industrialized scale. Among the private endeavors is the Novartis Insti-tute for Functional Genomics, founded by Novartis to identify and characterize targets using high-through-put technologies. In the biotech field, Structural Genomix aims to become a platform provider and generate revenues by selling protein structures; the company may also decide to exploit its data in-house, and extend into in silico drug design. But it might be several years before technologies have developed far enough for the necessary scale effects to be realized.
trials, if the company were able to decide in just one out of ten such cases to abandon development ear-lier, it could save an additional $100 million per drug.
Improving decision making to that extent will take more than simply acquiring and implementing the new genomics technologies and approaches. It will take some serious strategic rethinking too, and pos-sibly major organizational changes. Whether to keep all activities in-house, or seek partners, or buy in tar-gets or leads. How to redistribute resources, reassign personnel, and revise lines of communication and chains of command. Such operational and organiza-tional quandaries will be addressed in detail in the final chapter of this report.
We implemented a fast-in/fast-out decision policy about projects—if we didn’t have optimal condi-tions met in 18 months, we killed it. That made all the difference.
—Former executive, leading pharmaceutical company
Even the basic business skill of decision making, then, is not immune to the influence of genomics technology. Whatever other benefits it brings, genomics serves as a wake-up call across the indus-try, even for companies trying to shelter from the genomics revolution.
The Challenges
Although implementing genomics offers compa-nies great opportunities, it also presents them with formidable challenges. One of these is to ensure that the quality of the pipeline remains uncompro-mised. Another is to put the new technologies into efficient operation.
Maintaining Quality
If the potential productivity gains are to be fully realized, the post-genomics R&D pipeline will need to retain or improve its pre-genomics quality. Any decline in quality—the quality of targets and leads—would obviously have an adverse effect on productivity. The main threat to quality derives from the unorthodoxy, the unfamiliar nature, of so
many new targets. Entire target classes, previously unknown, will need investigating. The temptation to pursue leads prematurely is bound to arise, and quality control will need to be rigorously enforced to uphold the pipeline’s usual success rates.
In any given experiment, 70 percent of what I see is completely new. It could be a gold rush, or it could be junk—-there’s no way to tell until I sit at the bench and do more work.
—Director of research, leading biotech company
To appreciate the threat accurately, we need a proper definition of the term quality.
The “intrinsic quality” of a target or lead amounts to its likelihood of success, which is based on factors such as clinical relevance and drugability. Companies can do little to alter this type of quality. The “provisional quality” (or “informational qual-ity”) of a target or lead is based on the amount of data available on it at any given time—how much is known about its clinical relevance, drugability, and so on. (This informational quality helps to predict success rates, but does not influence them.) Companies can alter this type of quality, by spend-ing appropriately, and in that way can improve their ability to predict downstream success rates.
This distinction is crucial. But it has at times been overlooked, resulting in some confusion in the industry. A widely publicized concern has been that novel targets identified through genomics would tend to be of inherently lower quality than pre-genomics targets, and thus more likely to fail at some costly phase downstream. That inference is an oversimplification, and is misleading.
Certainly genomics proposes many more novel gets (as much as 60 to 70 percent of potential tar-gets, in our interviewees’ experience, may belong to previously unknown target classes), and their informational quality at that early stage is duly mod-est. But that says nothing about their intrinsic qual-ity. Any prudent company, no matter how bold, will strive to learn more about novel targets before deciding to pursue them downstream. In our analy-sis, investments made to raise a novel target’s
infor-mational quality to the level of a known target’s would be more than recouped in due course. The overall cost of these novel targets—raising their informational quality and then pursuing them down the value chain—is bound to rise initially. However, within three to five years from the initial discovery of a target in a novel class, according to our model, the overall cost increase per novel-class drug could return to average.
Where do the added costs come from? And what must happen to offset them?
The Cost of Quality Control
Our model predicts that the typical increase will be about $200 million and more than one year per drug (that is, a total cost of $790 million versus $590 million, and a total time to drug of 13.8 years versus 12.7 years). The increase is mainly attributa-ble to the extra time needed to understand target function and develop appropriate assays in target validation and screening; also, to the need to screen a higher proportion of compounds, since an appro-priate subset of a larger library cannot be selected in advance.
Chemical optimization costs would increase only if the novel target required a novel compound (by no means a necessary requirement, though certainly a possible one occasionally). Our model examines this worst-case scenario explicitly. If a novel target does happen to require a novel compound, or a compound unfamiliar to the medicinal chemists, the potential efficiency loss causes a further increase of $290 million and more than two years per drug (that is, a total cost of about $1.1 billion versus $590 million, and a total time to drug of 15 years versus 12.7 years). The additional increases here would be due to the extra time needed now for medicinal chemists to learn how to modify the com-pound and attain specific properties through trial and error. But this worst-case scenario should not be very common.
Moving further still down the value chain, to the preclinical and clinical phases, costs are not expected to increase. The downstream success rate
for novel compounds or targets should turn out to be much the same as that for known compounds or targets, as long as the same standards are applied. There should be no significant increase in toxicity or decrease in efficacy, other than in very unlikely circumstances—for instance, if existing animal models somehow proved less suitable, or if drugs for novel target classes were to interact with meta-bolic pathways in utterly unfamiliar ways.
Offsetting the Costs
Raising the informational quality of novel targets involves a heavy investment, but it is a wise in-vestment. And a fairly quick one: knowledge about one novel target quickly elucidates other poten-tial targets in the same class. Thanks to feedback loops, knowledge increases geometrically. As more is learned, the level of investment can tail off accordingly.
In any case, the alternatives to making that early investment in informational quality are far from attractive. On the one hand, dropping the targets would be terribly short-sighted: companies would be forgoing the opportunity to discover and exploit untapped sources of revenue. On the other hand, pushing novel targets onward without adequate information on them would almost certainly result in a higher failure rate downstream, with all the associated implications for cost. An increased fail-ure rate of just 10 percent across chemical opti-mization and all of development would on average increase costs by about $200 million per drug. To sum up, then: costs incurred early in the value chain (by information gathering) look preferable to those that would otherwise be incurred later (as the result of a higher downstream failure rate). All the more so, given that the early costs should soon begin falling (investment in information is almost always associated with an experience curve): as novel target classes become increasingly familiar, it will become increasingly efficient and economical to pursue new targets within those classes. So with proper handling, the burden of that early cost increase is just a short-term one, and the productiv-ity of genomics-driven R&D should soon return
almost to that of more familiar target classes. We estimate the time required for this is about three to five years from the discovery of a novel target, which is the amount of time it should take to com-plete validation and early screening (assay develop-ment). (See Exhibit 4.)
Putting the New Technology into Operation
It is one thing to acquire and install new capabili-ties and another to get them to function as they are meant to. The challenge of making genomics tech-nologies operational has two major components: easing the bottlenecks that will develop, and resolv-ing the personnel conundrums that are sure to arise.
The Problem of Bottlenecks
The bottlenecks result, in effect, from the uneven-ness of the efficiency gains at different points in the value chain. (See Exhibit 5.)
Consider the sixfold increase in target identifica-tion described above. This escalating quantity of targets could turn out to be not so much a glorious profusion as an exasperating glut. Unless there is some corresponding increase in the capacity to process them downstream, these targets will simply
loiter at their source in a wasteful logjam. Or con-sider chemical genomics. Implementing this approach will build up huge pressure on screening resources: sending unvalidated targets for screen-ing could involve a 120-fold increase. So too with efficiency gains at other points in the value chain: without the necessar y downstream adjustments, bottlenecks will inevitably develop.
Our capacity to do functional experiments was completely choked by potential targets.
—VP of discovery, major pharmaceutical company
But the problem is a dynamic one, and accordingly very awkward to deal with. Ease one bottleneck and you often create another downstream. Or ease it too much and you convert it into a bulge—over-EXHIBIT 4
IMPACT OF QUALITY ON COST TO DRUG
Mix of novel and known targets
Approximate cost ($M)
Novel targets only Novel targets and novel compounds only Benefits of experience over 3-5 years Pre-genomics Post-genomics 1,080 880 590 590 790
SOURCES: BCG analysis; industry interviews.
EXHIBIT 5
UNEVEN PRODUCTIVITY GAINS CREATE IMBALANCE
2,4001 108 138 72 30 7 Increased productivity Number today
to get one drug
Required productivity2 Not to scale Potential targets Validated targets Lead candidates Drug candidates INDs Drug Targets Compounds Poten-tial targets 400 ID Development Preclinical Clinical Chemistry Screening Optimization Biology
Target ID Target Validation
SOURCES: BCG analysis; industry interviews.
NOTE: Does not include impact of pharmacogenetics, to be addressed in next installment.
1Number of targets identified by investing same resources post-genomics as
pre-genomics.
resourced in relation to the flow from upstream, and hence wasteful once again. It will take some adroit adjustment of resources and processes along the value chain to restore a smooth flow.
This imbalance will affect incumbents—integrated companies with established value chains—worst of all. They have resources and processes in place; changes are likely to be difficult and disruptive. To implement the new genomics technologies is trou-blesome enough, but then to have to redistribute resources along the entire value chain will take real determination. (To other companies, by contrast, bottlenecks might represent very favorable oppor-tunities. In particular, genomics companies could benefit. (See sidebar, “Upstart Start-ups—the Competitors Classified.”)
The Human Factor
To flourish in the new genomics era, and possibly even to survive, companies are going to have to engage the new realities. It will not be easy. Some of the new technologies will tend to overstretch or even defy existing capabilities and organizational structures. All along the value chain, processes and resources are going to have to be adjusted.
The resources in question include human resources, and retrenching, reassigning, or supplementing tal-ented personnel is a far from straightforward proce-dure. But it will have to be done. Organizational restructuring is likely to entail distressing upheavals for corporate culture and personnel alike. The strategies adopted for managing it will require con-stant monitoring and fine-tuning. New modes of cross-functional collaboration may need to be insti-tuted, new incentives offered, and so on.
I spend half my time looking for talent that isn’t out there, and the other half worr ying where they would fit if I found them.
—Research director, leading biotech company
* * *
In sum, implementing genomics technology will be very tricky. It will almost certainly require a holistic,
cross-value-chain perspective. We will discuss poten-tial solutions to these operational challenges in the third chapter.
A Final Word
By engaging affirmatively with the brave new genomics world, companies are making it possible to increase R&D productivity substantially. They will bring to bear an array of industrialized processes, informatics, and rich data sets—a formidable com-bination that promises to boost efficiency, and even improve success rates, all along the value chain. Here we have discussed both the opportunities and the challenges that arise when a company adopts and implements genomics technologies that are available today.
The opportunities add up to potential savings of nearly $300 million per drug—about one-third of the cost—and the prospect of bringing each drug to market two years sooner. The challenges include managing quality control and dealing with unfamil-iar operational predicaments: bottlenecks along the pipeline and a host of organizational difficulties. But for companies that choose not to meet the genomics revolution head on, the challenge is even greater: they will be unable to compete. These com-panies do more than leave money on the table. They face the inevitability of being left behind. To reap maximum benefit from the new technolo-gies, companies will need to scrutinize their re-sources, processes, and policies throughout the value chain. Pharmaceutical and biotech managers will need to ask themselves some taxing questions as they begin to formulate their genomics strategy: • Which specific genomics technologies and
approaches make the most sense for our com-pany? What investments and capabilities would be needed to integrate these new technologies and approaches successfully?
• What capabilities do we already have? What investment are we prepared to make to acquire those we lack?
U P S T A R T S T A R T - U P S — T H E C O M P E T I T O R S C L A S S I F I E D
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 in-cumbents are easy to identify: the traditional phar-maceutical 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 dis-tantly 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 multi-tude of e-commerce practitioners. Examples of such companies are PE Biosystems, the supplier of high-throughput 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 attempt-ing to integrate and exploit those databases to con-duct 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 intermedi-ates—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 devel-oped proprietary high-throughput screening technol-ogy to exploit an opportunity in screening and chem-ical genomics. Another example is MorphoSys, which has developed a platform for rapid develop-ment of high-affinity antibodies, for use in target val-idation 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: con-centrating 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 tech-nologies or of information and knowledge) would appear to be well positioned if they target areas of scarcity (that is, bottlenecks) with proprietary
prod-• 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?
These questions can be addressed by thorough, thoughtful analysis. Key investment decisions will be required, as well as a carefully planned imple-mentation 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.
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 busi-ness model.
Meanwhile, the third group seems in the most prom-ising position. The pharmaceutical business remains attractive, with margins averaging more than 80 per-cent, so it is easy to see why so many genomics companies aspire to become drug companies. But
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 out-sourcing 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.
Preface
Having discussed the genomics wave in the previous chapter, and the way that it promises to enhance R&D productivity, we now turn to the genetics wave. Several broad differences suggest themselves imme-diately. Where the genomics wave is technology-driven, the genetics wave is better viewed as data-driven, exploiting the known details of the human genome and individual variations within it. Where the genomics wave brings benefits mainly at the drug-discovery and preclinical phases, the genetics wave will prove its worth in both the earliest phase and the later phases of the value chain—target dis-covery and the clinic. Where the genomics wave enhances R&D productivity mainly by securing great improvements in efficiency (with only modest improvements, if any, in success rates), the genetics wave could boost success rates dramatically as well. One further difference should be mentioned: where our model for the genomics wave was put for-ward with considerable confidence, our model for the genetics wave is more tentative. At this early stage, any assessment of genetics’ impact on the eco-nomics of R&D is bound to be provisional. Certainly genetics has huge potential: if all goes according to plan, it will change R&D productivity beyond recog-nition. But between that potential and its full real-ization lie several years and many obstacles.
The potential consists in tremendous savings. First, genetics can bring about great efficiency gains by making it possible to shorten or even bypass various steps in the value chain. Second, genetics holds the
prospect of transforming success rates: failures in the R&D pipeline currently account for 75 percent of the total cost to drug. But offsetting such oppor-tunities, dangers loom large. Riding the genetics wave involves a greater risk than riding the genomics wave alone—though it is more exhilarat-ing and, if the risks are successfully negotiated, ulti-mately more rewarding. How to choose between dis-cretion and valor is a crucial strategic decision that companies will have to make.
In analyzing the economic implications of genetics, this chapter of our report considers the effect only on pharmaceutical R&D. But genetics is likely to affect health care far beyond R&D, in both the short and the long term. In the short term, new market opportunities should arise in the formerly sleepy diagnostics sector. (Drug companies may or may not be able to exploit these opportunities: see sidebar, “Diagnostics—an Opportunity Too Good to Miss…and Perhaps Too Good to Grasp.”) In the longer term, genetics is likely to transform the delivery of health care. Increasingly, diseases will be redefined into various subtypes—a refinement that should facilitate more appropriate care and more “rational” drug design. The combination of new diagnostics, new disease definitions, and new tai-lored drugs should prove a winning one, and may well usher in an era of individualized medicine. R&D remains the focus of our analysis here, how-ever: specifically, the wide range of economic reac-tions that R&D might show under the impact of the new genetics information. We discuss the tremen-dous opportunities as well as the accompanying
Chapter 2: The Impact of Genetics
risks inherent in genetics-based R&D, and explore various ways of managing them.
Two Kinds of Genetics Approaches
There are two relevant approaches to consider when assessing the economic impact of genetics on
R&D: disease genetics and pharmacogenetics. They operate at different stages of the value chain.
Disease genetics is invoked earlier, during the
discov-ery phase: it involves the search for genes that make people susceptible to particular diseases, with the aim of then finding targets. Pharmacogenetics is the
D I A G N O S T I C S — A N O P P O R T U N I T Y T O O G O O D T O M I S S …
A N D P E R H A P S T O O G O O D T O G R A S P
It will be several years before genetics fulfills its promise. In the meantime, however, companies might begin to enjoy a preliminary reward, in the form of diagnostics—essentially a byproduct of their broader genomics research programs. Certainly diag-nostics is the subject of great expectations, though whether and how soon it will meet them remains to be seen.
Many research projects in genomics and genetics will devise diagnostic tests as a matter of course—in parallel with research or simply as a preliminary step, perhaps—without portraying them that way. Diagnostic tests can be understood in a fairly broad sense here. Disease genetics, for example, in identi-fying a target, is in effect finding a marker of disease susceptibility. Expression profiling, in identifying the molecular differences characterizing a disease’s dif-ferent subtypes, is pointing the way to difdif-ferentiated and fine-tuned therapies. And pharmacogenetics, in identifying variations in drug response among vari-ous patients, could be helping to suggest the most suitable drug for them.
The opportunities inherent in diagnostics will appeal to drug companies at several levels. First, costs are low. The intellectual capital needed to develop a di-agnostic test comes free, courtesy of existing re-search in drug discovery and development; validation studies can be run in parallel with drug efficacy stud-ies, or perhaps can even simply borrow their results and extrapolate from them; and as for safety studies, diagnostic tests don’t need any. All in all, then, the incremental spending required to develop a mar-ketable diagnostic test is, relatively speaking, paltry.
Second, rewards are prompt. Diagnostics, in bypass-ing most of the traditional steps of pharmaceutical R&D, can be brought to market not only far more cheaply than drugs, but far more quickly too. Drug companies are thereby able to realize some unusu-ally fast payback on their R&D spending.
Third, the market outlook is favorable. As new thera-pies proliferate, more diagnostics will be demanded; and as technologies advance, new types of diagnos-tics will become available. The signs are good. These opportunities are to some extent offset, how-ever, if not by risks, then at least by challenges. There is the challenge of novelty, for instance: for many traditional companies, diagnostics would involve manufacturing an unfamiliar kind of product— a kit—and that in turn would involve developing new capabilities, or else partnering with a dedicated diag-nostics company. Companies that have an in-house diagnostics division, such as Hoffmann-La Roche, Abbott, and Bayer, will have an advantage here. Then there is the challenge of intellectual-property rights: companies might find it more difficult to assert those rights over diagnostics than over their findings in pharmaceutical research.
Perhaps the most daunting challenge is timing: diag-nostic tests will tend to emerge too speedily, becom-ing available sooner than the therapies they indicate. So the chief appeal of investing in diagnostics—its prompt availability—may be undercut. Drug compa-nies may have to delay marketing their diagnostics (and thus delay capitalizing on the opportunities) until their drug R&D pipelines catch up.
genetics-based form of pharmacogenomics (see sidebar, “Pharmacogenomics—Some Definitions”), and comes into play later, in the development phase: it involves predicting the efficacy and side effects of candidate drugs.
The data explosion detonated by genomics tech-nology has created vast amounts of genetic infor-mation, ready for sifting. The findings of the Human Genome Project and related endeavors are merely the starting point. The ultimate goal is to elucidate the genetic basis of human disease and drug response. In the short term, genetics research will enable scientists to predict disease susceptibility and likely drug response in individuals; in the longer term, it should help to improve the quality of pharmaceuticals and medical diagnoses.
Attaining the short-term goal is, conceptually, sim-ple enough. The genetic codes of individuals differ in tiny, but sometimes decisive, details. By compar-ing an individual’s genetic variations against the “standard” genome, scientists should be able to pre-dict whether that individual is at risk for a specific disease, and, if so, how well suited he or she is to a particular drug therapy—the work respectively of disease genetics and pharmacogenetics.
The two approaches benefit R&D economics in dif-ferent ways. Disease genetics will improve efficiency in target discovery and, by leading to the discovery of particularly high-quality targets, will bring about improved success rates in validation and down-stream. Pharmacogenetics, by enabling scientists to select patients more appropriately for clinical trials,
P H A R M A C O G E N O M I C S — S O M E D E F I N I T I O N S
Pharmacogenomics is the use of genomics approaches to elucidate drug response. There are three relevant approaches: via DNA, via RNA, and via proteins, and three corresponding forms of phar-macogenomics: pharmacogenomics using genetic approaches (or pharmacogenetics), expression filing (or expression pharmacogenomics), and pro-teomics (or proteomic pharmacogenomics).
Pharmacogenetics predicts patients’ drug response
by analyzing the genetic variations in their DNA. It is the form of pharmacogenomics discussed in the main text here.
Expression pharmacogenomics predicts patients’
drug response by analyzing their RNA levels—specif-ically, by comparing the amounts of RNA found in different samples to determine which genes are expressed at different levels. An example: a research group at The Whitehead Institute studying two very similar leukemias (AML and ALL) has observed a distinct difference in expression levels of specific genes, and thereby provided a quick and reliable method for differentiating them. Patients are now less at risk of being misdiagnosed and being given
an incorrect, and possibly lethal, drug treatment: in effect, the test screens for adverse drug response. Expression pharmacogenomics seems to be moving from academic studies and biotechs into more main-stream pharmaceutical R&D. Witness the recent pur-chase by Merck and Co. of Rosetta Inpharmatics, a biotech founded specifically to develop expression pharmacogenomics.
Finally, proteomic pharmacogenomics predicts patients’ drug response by analyzing their protein levels—specifically, by comparing protein readings in different tissue samples to identify proteins that differ either in structure or in expression levels. Consider the example of an aberrant fusion of two proteins called Bcr and Abl, which occurs in more than 95 percent of patients with CML (chronic myeloid leukemia, which accounts for about 20 per-cent of all cases of adult leukemia). This aberrant fusion protein is present only in cancer cells. It dis-tinguishes itself from its normal counterparts by its increased size. It can be used not only to monitor the progression of the disease but also to test whether Gleevec, a revolutionary new drug, would provide an effective therapy.
