市場調査報告書：薬理ゲノム・ビジネス : ファーマコゲノミクス

Executive Summary

This report captures the perspectives, insights and discussions presented by industry leaders at two recent conferences on pharmacogenomics (December 1999 and 2000), organized by The Center for Business Intelligence (CBI). The report focuses on four areas: 1) key drivers for the adoption of pharmacogenomics in pharmaceutical R&D and medical practice; 2) legal, ethical and regulatory considerations in commercializing pharmacogenomics; 3) integrating pharmacogenomics into pharmaceutical R&D, from drug discovery to clinical trials and postmarketing studies, to develop better and safer drugs and to increase pharmaceutical R&D productivity; and 4) the impact of disease market microsegmentation through individualized medicine.

Big Pharma's Approaches

Genetics is not a new scientific discipline, but a rather old concept that has been in existence and applied in biology for a while. What is new about genetics today is the ability to use it as a powerful tool in biomedical research. In Chapter 3, Dr. Klaus Lindpaintner of Roche Genetics discusses the role of pharmacoepidemiology in understanding disease risk in individual patients and specific patient subgroups and in designing pharmacogenomics-based medicines of the future. He emphasizes that genetics will neither revolutionize medicine nor change medical practice conceptually. Rather, modern genetics will dramatically impact medicine in an evolutionary stepwise manner, ultimately leading to individualized healthcare. The greatest and most immediate impact of pharmacogenomics will be in the area of molecular disease diagnosis. However, there seems to be a concern in industry that it will be easier to advance genomics-related technologies than to develop and adopt meaningful applications of genetic tools and concepts, although it is the latter that are required for widespread use of pharmacogenomics in medical practice.

In Chapter 4, Dr. Elliott Sigal of Bristol-Myers Squibb describes how the pharmaceutical industry has used genetics in the past to develop therapeutics and find new disease targets. For example, his own company has made a major commitment to pharmacogenomics in its oncology program to identify novel targets and promising lead candidates, to evaluate existing cancer drugs and to use predictive testing to improve cancer diagnosis and treatment. Specifically, Dr. Sigal describes how differential expression profiling has reached the proof-of-concept stage and demonstrated its use in accurate classification of cancer subtypes, including leukemias, lymphomas and solid tumors, such as brain tumors and melanomas. The idea here is that molecular disease classification will lead to the development of novel therapeutics specific for the disease subtype and that existing therapies will be tailored toward prequalified patients who have a higher chance to respond to the drug. Ultimately, an integration of cancer therapeutics with molecular diagnostics, such as genomic expression profiling, will lead to improved cancer treatments.

Craig Fitzgerald of GlaxoSmithKline discusses in Chapter 5 how a phased-in implementation of pharmacogenomics will change the way pharmaceutical companies conduct business. In a first stage, pharmacogenomics will be used for product differentiation to back up superiority claims. In a second stage, it will be used to predict individual drug responses regarding safety and efficacy of drug treatments. While important opportunities, these first two applications of pharmacogenomics will lend themselves to traditional marketing approaches. It is predictive medicine, the third and last application of pharmacogenomics that will transform the way pharmaceutical companies market their products. Predictive medicine tailored to the individual offers mass-customized marketing approaches that will require novel marketing capabilities and a novel type of marketing and sales organizations. In a new world in which therapeutics and molecular diagnostics are integrated, the consumer (patient) will become increasingly involved in treatment decisions through the process of disintermediation, or the elimination of a mediator (in this case, the physician).

In Chapter 6, Dr. Philip Ma of McKinsey & Company describes the company's approach of using pharmacoeconomic modeling to analyze the benefits and risks of applying pharmacogenomics in pharmaceutical R&D. The potential benefits of developing more efficacious and safer drugs and improving R&D productivity need to be balanced with the downside risks of microsegmenting key markets, intellectual property entanglements and uncertain regulatory requirements. However, Dr. Ma points out that if a disease market can be microsegmented, the market eventually will segment itself, with or without the use of pharmacogenomics. A case study with Pravastatin (a cholesterol-lowering drug) indicates that pharmacogenomic claims over time can result in a two-fold increase in sales revenues for best-in-class compounds. On the downside, second-in-class drugs may experience a two- to three-fold decrease in revenues. Hence, pharmaceutical companies need to be very strategic about controlling the trend toward the integration of therapeutics and genetic diagnostics.

The establishment of The SNP Consortium has been a unique initiative among leading pharmaceutical companies, academic centers and the Wellcome Trust to create a high-quality, genome-wide map of human SNPs that will be available to the public at no cost. Major drivers behind this effort were 1) concerns about intellectual property issues that could have impaired genomics research in the future and 2) the need for a high-quality, standardized map as a reference point for biomedical research worldwide. In Chapter 7, Arthur Holden and Dr. David Wang of The SNP Consortium describe the impressive progress of this 2-year effort that by far exceeded the original goal of mapping 300,000 SNPs. In October 2000, the identification and validation of more than 1.4 million human SNPs was announced. This remarkable SNP collection now can be used to carry out genome-wide association studies to identify and map human disease genes. In addition, the establishment and success of this consortium illustrates a recent trend in industry toward approaching large-scale R&D projects by pooling resources, capabilities and expertise required to carry out highly complex and risky research undertakings.

Approaches of Platform Technology Companies

In Chapter 8, Dr. Roy Pettipher of Oxagen discusses the use of pharmacogenomics for target identification and validation to select and develop high-quality drug candidates. He argues that the recent finding of widespread and significant interindividual genetic variability has enormous implications for drug discovery. On the one hand, the industry can use genetic variability to identify causative disease genes, thereby enlarging the rather limited pool of traditional drug targets. At the same time, the industry can use genetic variability of the drug targets when prioritizing and optimizing drug candidates. In the long term, pharmacogenomics can help companies manage the risk of pharmaceutical R&D by decreasing the probability of clinical trial failure due to incorrect disease hypotheses. Furthermore, the integration of pharmacogenomic analyses into R&D will help companies fail compounds earlier and develop higher-quality drugs.

While the long-term goal of pharmacogenomics is individualized medicine, short-term applications are in the area of drug discovery and clinical development, and possibly postmarketing studies. Dr. Mark Furth of PPGx and Michael Murphy, formerly of that company, explain in Chapter 9 that pharmacogenomic profiling in clinical trials can save hundreds of million dollars in clinical trial costs. In addition, this approach can ensure a representative and known study population where slow or ultrarapid metabolizers no longer are defined as outliers, but as individuals with their own pharmacokinetic profiles that require individualized drug dosing and treatments. However, they warn that setting up a genetic testing laboratory is a very complex endeavor that, in addition to technical capabilities, entails addressing issues of informed consent, confidentiality of medical records, and genetic counseling. For example, a patient who is a slow metabolizer may also have an increased risk for developing certain types of cancer. In this case, it is not only difficult to communicate to an individual patient his/her statistically increased disease risk, but the implications of the genetic analysis go far beyond the initial purpose of the test (namely, to establish an individual's pharmacokinetic profile).

There has been a lot of debate about whether linkage disequilibrium (LD) can be used to identify and map disease-causing genes. Much of this debate has focused on the size of LD blocks or haplotypes, that is, the size of chromosomal DNA that is inherited 兎n bloc・linking genetic markers within that chromosomal region. A most recent study published in May 2001 showed that the size of haplotypes or LD blocs is larger than previously thought and that it varies greatly along ethnic lines. These findings indicate that SNP analysis to find disease genes is feasible but requires a careful selection of the population used in such studies. One company that has already adopted such an approach is Genaissance Pharmaceuticals. In Chapter 10, Dr. Clay Stephens describes his company's approach that is based on population-genomic concepts and informatics capabilities. He presents an illustrative case study that uses haplometrics (marker cluster analysis) to organize SNP markers into haplotypes to predict drug response. Rather small clinical studies could demonstrate that haplotype markers at the locus of the beta-2 adrenergic receptor are predictive of drug response to albuterol in asthma patients.

Genomic technology holds the promise of linking therapeutics and molecular diagnostics in a way that will transform the diagnostics market, create value for the diagnostics industry and ultimately change the way medicine is practiced. In Chapter 11, Ken Conway of Millennium Predictive Medicine explains the concept of diagnomics ・the integration of therapeutics and molecular diagnostics ・using the example of melanoma diagnosis. In this case, expression profiling of melastatin, a molecular marker for the progression of melanoma, can be used as a molecular diagnostic test to distinguish between metastatic and nonmetastatic melanomas. This case study establishes proof-of-concept of genomics-based molecular diagnostics and illustrates the potential use of molecular diagnostics in clinical decision-making. The business model of the diagnostic division of Millennium is based on the notion that molecular diagnostics have the ability to add medical and economic value that will lead to a significant increase in revenues from diagnostics. The company predicts that the current market share for diagnostics will increase from the current 1% of the total health care dollars to an estimated 5%.

Perspectives of Third-Party Stakeholders

Adoption of pharmacogenomics will require the participation of many players in the health care arena. One such player is the central reference laboratory, whose most important role is the standardization of diagnostic assays into tests that can be adopted for high-throughput and low-cost formats. In Chapter 12, Dr. Steven Anderson of LabCorp discusses the role of the central reference lab in fostering the acceptance of molecular diagnostics and other novel diagnostic technologies in routine medical care. Using the example of the HER/neu receptor assay in breast cancer, he illustrates the various issues that need to be addressed when commercializing a molecular diagnostic test. He further stresses that the choice of a diagnostic assay critically depends on the understanding of the clinical significance of a molecular marker and its role in the disease.

In order to create commercial value through pharmacogenomics, the technology needs to move into applications in the real world. For example, in Part I of Chapter 13, Dr. William Wardell from Covance stresses the importance of incorporating pharmacogenomic studies into clinical development in a way that allows the inclusion of pharmacogenomic superiority claims into drug labels. He says, 典he name of the game in drug development is getting a good label for a drug when the compound undergoes clinical development.・Building on this perspective, in Part II of Chapter 13, Dr. Steven Burch of Strategic Outcomes Services discusses the need and strategies for demonstrating value of pharmacogenomics-based treatments and diagnostics. Value in this context has three dimensions that include clinical, economic and humanistic benefits. He points out that the pharmaceutical industry has traditionally been focused on demonstrating clinical benefits of a drug and that the industry is still learning how to most effectively show economic and humanistic benefits of new medical treatments.

In Chapter 14, Dr. Peter Juhn, formerly of Kaiser Permanente, discusses the implications of pharmacogenomics for the health care industry. He asserts that managed care organizations have not embraced new medical technologies and that they could play an important role in the adoption of new medical technologies that hold the promise of improving medical outcome and reducing overall health care costs. To illustrate this point, he describes how Kaiser Permanente, the largest private health care delivery organization in the U.S., established a separate R&D unit within the organization in order to evaluate and implement novel medical technologies. Specifically, he describes how, in the area of pharmacogenomics, the Kaiser Permanente Care Management Institute has established several programs to study the effect of genetics on drug response in disease areas such as asthma, hypertension, AIDS, cancer and coronary artery disease. However, like any other technology, pharmacogenomics will have to make a strong case for efficacy and cost-effectiveness to establish a presence in the ever-changing and competitive environment of health care delivery.

Regulatory, Legal and Ethical Issues

The FDA rarely takes a proactive position in defining the regulatory impact of new technologies. Rather, the agency tends to observe and wait until the industry works out its own new paradigms before generating new regulations. In Part I of Chapter 15, Dr. Curtis Scribner, former Deputy Director at the CBER, describes some of the regulatory issues of pharmacogenomics and the current regulatory climate. At the moment, regulatory requirements hardly represent an insurmountable barrier to entry, but they need to be carefully considered when developing a genetics-based product or service. Particularly complex are situations in which a genetic test is integrated into drug development. He emphasizes that it is the obligation of the industry to work with the FDA to set reasonable standards and criteria to work through some of these complex regulatory issues. In Part II of Chapter 15, Karen Hedine from Qiagen Genomics discuses some of the current strategies that allow genomics companies to adhere to and define the evolving regulatory requirements in genomics. She stresses that companies need to build in-house regulatory expertise to monitor evolving regulations and to help the FDA determine how the regulatory arena will play out in pharmacogenomics.

The emergence of biotechnology over the past decades has been a difficult test for the patent law. Genomics-related inventions challenged the traditional patent law and changes to the law continue to evolve. In Chapter 16, Dr. Cathryn Campbell form Campbell & Flores explains that much of the recent controversy in patent law focused on the utility requirement. Traditionally, the patent office did not put much emphasis on this aspect of the law, because there was no need to consider the usefulness of a mechanical device, such as a bicycle ・nobody would have applied for a patent for a bicycle that did not work. By contrast, in the early days of biotechnology, claims were made for genes, expressed sequence tags (ESTs) or SNPs without any understanding of their function or possible usefulness. Today, the patent office makes the utility requirement one of the central requirements when considering a genomics-related patent application. For example, if an inventor can show how a SNP can be used as a genetic marker to stratify patient populations, then the claim might be patentable. Another change in patent law relates to reach-through claims. Although the usefulness of a genomics-related invention needs to be evident, as a rule, the patent office does no longer extend patent claims to things discovered through the original invention.

Many people regard their genetic makeup as special and deeply personal, embodying part of their very essence. As Stanford University's Henry Greely discusses in Chapter 17, pharmacogenomics raises a number of ethical issues both for the research itself and for its clinical applications. Research-related issues include informed consent, communicating relevant information on genetic risks, group consultation and consent, confidentiality and profit sharing. Identifying disease-susceptibility or drug-response genes raises the possibility that scarce medical treatments may be allocated on the basis of an individual's genotype. However, it also may help society to abandon certain racial associations in medicine and instead screen directly for probable outcomes. The good news is that by starting now to educate the medical establishment, the media and the public, we can help prevent abuses arising from fears and misunderstanding over the role of genes in disease and therapy.

薬理ゲノム・ビジネス : ファーマコゲノミクス

The Business Case for Pharmacogenomics 2nd edition

Executive Summary

Table of Contents

1. Executive Summary

2. Introduction

3. The Role of Epidemiology and Genetics in Pharmaceutical R&D

4. The Impact of Pharmacogenomics in Oncology

5. Pharmacogenomics: The Trend Toward Mass-Customization

6. The Pharmacogenomics Opportunity: How and When to Become a Player

7. The SNP Consortium

8. The Use of Pharmacogenomics During Drug Discovery

9. Pharmacogenomic Profiling to Assess Drug Response

10. Population Genomics and Informatics: A Haplotype-Based Approach

11. Diagnomics: Integration of Therapeutics and Diagnostics

12. The Role of the Central Reference Lab in Pharmacogenomic Studies

13. Commercial Value Creation Through Pharmacogenomics

14. Adoption of Pharmacogenomics in Health Care Delivery

15. Regulatory Implications of Pharmacogenomics

16. Legal Issues Associated with Pharmacogenomics

17. Individualizing Medicine through Pharmacogenomics: Ethical Concerns