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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease 1 Introduction Biomarkers are tools used by doctors, scientists, and other health professionals to obtain information about a patient’s or research subject’s health status or response to interventions. Many medical or lifestyle interventions, indispensible to modern medical care, can induce changes in biomarkers. In order for consumers, physicians, drug developers, and policy makers to make informed decisions based on biomarkers, it is important to understand the amount, strength, and quality of data supporting the use of any specific biomarker to direct decisions in clinical care, drug development, public health, and health policy decisions. Every time a parent takes a child’s temperature looking for a fever, they are using a biomarker to assess for illness. That parent may go on to monitor their child’s temperature over the course of several days, both to follow the progression of an infection and to determine whether antipyretic and antimicrobial therapies are working effectively. Even this fairly simple example of a biomarker highlights some of the issues associated with their use. For example, the method used to measure body temperature matters. Using a thermometer is a more accurate approach than a hand to the forehead. Slightly different temperatures will be obtained depending on whether the measurement is an oral, ear, rectal, or axillary temperature. Although a fever is a useful piece of information about how a disease process is developing, it is only one piece of information in what could be a complex illness. To further complicate matters, some diseases present with relapsing and remitting fevers, and interpretation of temperature data in that patient population needs to be very different
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease than an illness where a fever accompanies acute infection and resolution of the fever signals a shift to resolving the infection. In an ideal setting, biomarkers reflect disease course and activity; many good biomarkers are useful in monitoring disease process and complications. In the diagnosis and management of prostate cancer, for example, prostate-specific antigen (PSA) can be measured in a patient’s blood, and PSA levels can be followed as an indicator of whether the cancer is growing or responding to treatment. However, this example illustrates several challenges of using biomarkers. PSA may be elevated in some patients because they have prostate cancer, but it can also be elevated for other reasons. One important finding that has been reported recently is that PSA is not necessarily a good biomarker for population-wide screening for prostate cancer (Sardana et al., 2008). This illustrates the point that biomarkers are effective only to the degree that they are used in the appropriate context. It is critical to note that even a perfect biomarker cannot, with certainty, be used in place of patient outcomes in the evaluation of an intervention. One step in supporting regulators is to institute an evidence-based, transparent process for biomarker evaluation. Biomarker evaluation is often thought of as two unlinked steps: analytical validation of biomarker tests and biomarker qualification. Biomarker qualification is the evidence-based process of linking a biomarker with one or more clinical endpoints. Decisions to use biomarkers are dependent on the intended applications. Currently, the evaluation of biomarkers is not based on uniform standards or processes, but rather on the gradual development of consensus in the scientific community. The potential value and impact of a more uniform and transparent evaluation process was noted in the 2007 Institute of Medicine (IOM) report, Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment (IOM, 2007), which recommended that government agencies and non-governmental stakeholders “should work together to develop a transparent process for creating well-defined consensus standards and guidelines for biomarker development, validation, qualification, and use to reduce the uncertainty in the process of development and adoption.” The Cancer Biomarkers recommendation gains even more weight when considered with the emergence of pharmacogenetics, pharmacogenomics, and all of the promising medical breakthroughs of personalized medicine. Pharmacogenetics is the science of understanding how an individual’s genes may interact to impact drug function and metabolism. Personalized determination of drugs that will work for given patients and dosing based on their metabolic profiles has the potential to decrease unnecessary or not helpful treatments and decrease adverse effects from treatments when they are helpful. Pharmacogenomics is the science of understanding genetic variations between populations in disease incidence, progression,
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease and treatment. More detailed understanding of disease biology has the potential to lead to more effective prevention and treatment approaches. Biomarkers are critical to progress in these areas, and it will be important that newly discovered biomarkers be adequately studied before being adopted into routine clinical management of patients. ORIGIN OF THE TASK In 2008, the Food and Drug Administration’s (FDA’s) Center for Food Safety and Applied Nutrition (CFSAN), in conjunction with the FDA’s Center for Drug Evaluation and Research, approached the IOM for advice on the topic of biomarker and surrogate endpoint evaluation, noting the limited number of surrogate endpoints available, the high cost of evaluating possible surrogate endpoints biomarkers, and the absence of an agreed-upon, systematic, transparent process for biomarker evaluation. Study developers were also interested in learning whether principles of biomarker qualification or evaluation learned in the drug development setting would also be generally applicable in other FDA-regulated product categories, such as foods and supplements. As part of its efforts within the Critical Path Initiative (CPI),1 CFSAN requested that the IOM charge an expert committee with the following task: An Institute of Medicine (IOM) committee will be convened to generate recommendations on the qualification process for biomarkers, with a focus on risk biomarkers and surrogate endpoints in chronic disease. These recommendations will consider existing prototypes for qualification of biomarkers used in drug development. The committee will recommend a framework for qualification and test it using case studies of risk biomarkers and surrogate endpoints for coronary heart disease (CHD) such as low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol levels. In particular, the committee will: Conduct a review of current approaches to qualifying biomarkers. Recommend a framework that can be used to rank biomarkers according to the types and quality of evidence, considering context of use for a range of product types. Demonstrate applications through case studies. Make ancillary recommendations for the application, enhanced development, and use of risk biomarkers and surrogate endpoints in chronic disease.2 1 See http://www.fda.gov/oc/initiatives/criticalpath. 2 The terminology in the statement of task differs in a few ways from the terminology of this report. As will be explained in Chapter 3, the committee’s terminology replaces qualification with evaluation in many instances, and risk biomarker with biomarker.
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease CPI is a framework created by the FDA under which the challenges posed by increasing medical product development costs and lengthening time-to-market for medical products can be addressed. The need for improvement in the process for evaluation of biomarkers and surrogate endpoints was identified from the inception of the CPI at the FDA (FDA, 2004a), and formally recognized as a “Critical Path Opportunity” at CFSAN shortly thereafter (FDA, 2006). The following is an excerpt from the report published in June 2008 describing CFSAN’s 2007 progress in this area (FDA, 2008): [The] FDA is exploring development of a framework for validating modifiable risk factors (biomarkers) for chronic diseases, such as cancer, heart disease, diabetes, and others that can be the subject of a health claim. The framework will consist of defining the level and type of evidence that is required to support a biomarker that modifies the risk of disease. The first step toward defining a framework will consist of working through the National Academy of Sciences, Institute of Medicine, to convene a panel of experts to outline the steps necessary for qualifying a biomarker for evidence-based decision making, assuming funding becomes available. The task for the panel will be to hold workshops as needed and then to issue a report that [the] FDA can use in its review of scientific evidence offered to substantiate health claims that can be used on food products, including dietary supplements. Funds from the Critical Path [I]nitiative have enabled CFSAN to develop a task order with IOM for this initiative. Biomarkers and the FDA With regard to biomarkers, the FDA is subject to competing forces and is expected to evaluate many factors with a limited number of resources. The desire for effective new drugs, devices, and biologics accompanied by the goal of reducing the monetary cost and time expended on development of interventions for chronic diseases serve as incentives for more aggressive use of biomarkers (IOM, 2006). The need to protect patients and consumers from undefined risks is an incentive for more conservative use of efficacy biomarkers and for the development of effective safety biomarkers. Little consistent, reliable information is currently available regarding how consumers can know which foods might have health benefits beyond basic nutrition. Recently questions have arisen related to use of biomarkers in substantiating health claims about foods, namely whether the use of biomarkers to draw conclusions about the health benefits of nutrients, foods, and supplements should be encouraged, and how information about the uncertainty associated with using biomarkers in this way can be communicated to consumers.
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease Drug development costs have been estimated at $500 million to $2 billion per product depending on the size of the pharmaceutical company (Adams and Brantner, 2006). CPI began a few years after the implementation of accelerated approval regulations, and it identified a need for more biomarkers of efficacy. Public–private partnerships, such as the Critical Path Institute and the Biomarkers Consortium, were formed, in part, to foster precompetitive data sharing related to biomarker development. The Biomarkers Consortium3 has brought together industry, academia, the FDA, the National Institutes of Health (NIH), and the Centers for Medicare & Medicaid Services to identify and address areas of greatest potential impact in the need for new qualified biomarkers. However, their focus is primarily on facilitating the discovery of new biomarkers. As a result they have not made it a priority to propose an evaluation framework for biomarkers. At the start of CPI in 2004, it was estimated that only 8 percent of medicinal compounds reaching phase I clinical trials would eventually be approved for marketing (FDA, 2004b). One of the primary ways that CPI proposed to speed approvals was through the use of biomarkers. With accelerated approval came a greater need for postmarket studies of approved medicinal products. The FDA has faced and attempted to resolve some administrative challenges, such as manufacturers’ nondisclosure and/or underreporting of adverse events that result from product usage; inadequate resources to strengthen and broaden oversight efforts; and antiquated information technology systems, in effectively requesting and enforcing these studies, as will be discussed in Chapter 5. Nutrients, foods, and supplements are regulated under a different framework than are drugs, devices, and biologics. The FDA regulates products purchased with one out of every four consumer dollars spent. Of this amount, 75 percent is spent on products regulated by CFSAN: foods, supplements, and cosmetics. CFSAN’s $470 million budget regulates the $525 billion food and cosmetics industry (FDA, 2009a). Foods do not undergo premarket evaluation. New ingredients are evaluated, but for safety only. CFSAN also regulates the labeling of foods. This includes the familiar nutrition facts panel as well as a variety of health-related claims found on food labels and promotional materials. To a certain extent, the FDA’s evaluation of health claims has been crippled by the lack of an agreed-upon, transparent process for biomarker evaluation. Authorized and qualified health claims, which describe links between a food substance and a reduction in risk for a disease, may include data based on the measurement of surrogate endpoints or risk biomarkers as justification for the claims. It is uncommon for produc- 3 See http://www.biomarkersconsortium.org.
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease ers of foods or supplements to study the effects of foods and nutrients on clinical endpoints, which makes data from surrogate endpoints and biomarkers the focus of applications for health claims. These include folic acid for reducing the risk for neural tube defects and soluble oat fiber for reducing the risk of heart disease. Claims must be evaluated and authorized by the FDA in most cases. In some cases, health claims can be authorized based on a statement from an authoritative body, such as the NIH or the National Academy of Sciences.4 The lack of an agreed-upon, transparent process for biomarker evaluation has been seen as one of the roadblocks to a broader selection of surrogate endpoints on which claims could be based. DEFINITIONS The committee observed a great deal of inconsistent and imprecise definition and use of terms relevant to biomarkers and biomarker evaluation. Consistent, precise definition and use of terms is critical for biomarker evaluation because it is a topic important across many disciplines and has been for several decades. The committee has attempted to be consistent with the spirit of previous efforts at standardizing the language used with reference to biomarker evaluation, and clarifies several definitions where there is overlap or potential for confusion. Several of the definitions used in the report summary (see Box 1-1 below) deserve further discussion. A definition of risk biomarker, used in the statement of task, is also defined in Box 1-1. The definition of the term “biomarker” itself is not controversial. The definition provided by the Biomarkers Definitions Working Group is widely used, and other definitions do not differ fundamentally. The Cancer Biomarkers report presented two tables showing uses of biomarkers in clinical and drug development settings (see Tables 1-1 and 1-2). The committee viewed results from imaging tests as biomarkers because they are measurements that indicate normal biological processes, predict risk for disease, and monitor pathogenic processes and pharmacologic responses to therapeutic interventions. The committee also viewed genes, genetic signatures, and genetic mutations as biomarkers. While these are typically not modifiable, they do fulfill the Biomarkers Definitions Working Group definition of a biomarker, as they indicate normal biological processes, pathogenic processes, or pharmacologic responses. The statement of task for this study cites “risk biomarkers” for chronic disease. The committee defines a risk biomarker as a biomarker that 4 In legislation, the term National Academy of Sciences refers to the whole of the National Academies.
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease BOX 1-1 Important Definitions Analytical Validation: “assessing [an] assay and its measurement performance characteristics, determining the range of conditions under which the assay will give reproducible and accurate data.”a Biomarker: “a characteristic that is objectivelyb measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a[n] … intervention.”c Example: cholesterol level. Chronic Disease: a culmination of a series of pathogenic processes in response to internal or external stimuli over time that results in a clinical diagnosis/ailment and health outcomes. Example: diabetes. Clinical Endpoint: “a characteristic or variable that reflects how a patient [or consumer] feels, functions, or survives.”c Example: death. Fit-for-Purpose: being guided by the principle that an evaluation process is tailored to the degree of certainty required for the use proposed. Qualification: “evidentiary process of linking a biomarker with biological processes and clinical endpoints.”d Surrogate Endpoint: “a biomarker that is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence.”c Example: blood pressure for trials of several classes of antihypertensive drugs.e NOTES: b The committee defines “objectively” to mean “reliably and accurately.” e Please see Chapter 2 for discussion of this biomarker. SOURCES: a Wagner (2002); c Biomarkers Definitions Working Group (2001); and d Wagner (2008). indicates a risk factor for a disease. In other words, it is a biomarker that indicates a component of an individual’s level of risk for developing a disease or level of risk for developing complications of a disease. The committee viewed risk biomarkers as a subset of risk factors. Risk factors are variables that correlate with incidence of a disease or condition. Risk factors include social and environmental factors in addition to biological factors. Risk biomarkers are also to be distinguished from biomarkers of exposure used in toxicology, which were defined by the National Research Council as “the chemical or its metabolite or the product of an interaction between a chemical and some target molecule or cell that is measured in a compartment in an organism” (NRC, 2006; WHO, 2001). In its Guidance for Industry: Evidence-Based Review System for the Scientific Evaluation of Health Claims, CFSAN defined risk biomarkers as “biologi-
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease TABLE 1-1 Use of Biomarkers in Chronic Disease Patient Care Clinical Biomarker Use Clinical Objective Disease risk stratification Assess the likelihood that the disease will develop (or recur) Prevention Identify and track risk factors Screeninga Detect and treat early-stage disease in the asymptomatic population Diagnosis Definitively establish the presence of disease Classificationb Classify patients by disease subset Prognosis Predict the probable outcome of disease to determine the aggressiveness of treatment Prediction/treatment stratificationb Predict response to particular therapies and choose the drug that is mostly likely to yield a favorable response in a given patient Therapy-related risk managementa Identify patients with a high probability of adverse effects of a treatment Therapy monitoringc Determine whether a therapy is having the intended effect on a disease and whether adverse effects arise Surveillance Early detection and treatment of advancing disease or complications NOTES: a In toxicology, biomarkers of exposure help predict an individual’s risk of suffering consequences from exposure to a foreign substance. Exposure biomarkers are a subset of these two categories. b Companion diagnostic biomarkers include features from several of these categories. These tests identify whether an individual’s molecular profile associated with a disease pathophysiology is likely to respond favorably to a particular therapeutic. Examples include KRAS–cetuximab, HER-2–herceptin, and estrogen receptor status–tamoxifen. c Dose optimization is a subset of this category. SOURCE: Adapted from IOM (2007). TABLE 1-2 Use of Biomarkers in Drug Development Biomarker Use Drug Development Objective Target validation Demonstrate that a potential drug target plays a key role in the disease process Early compound screening Identify compounds with the most promise for efficacy and safety Pharmacodynamic assays Determine drug activity; select dose and schedule Patient selection In clinical trials, patient selection (inclusion/exclusion) by disease subset or probability of response/adverse events Surrogate endpoint Use of a short-term outcome measure in place of the long-term primary endpoint to determine more quickly whether the treatment is efficacious and safe in drug regulatory approval SOURCE: IOM (2007).
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease cal indicators that signal a changed physiological state that is associated with the risk of a disease” (CFSAN, 2009). This definition is narrower than the committee’s because it would seem not to include genetic risk factors and other situations that may be present in an individual from birth. Many risk biomarkers are not modifiable in beneficial ways, even when only ones indicating changed physiological states are considered. It is important to note that while some so-called risk biomarkers have been used as surrogate endpoints, risk biomarkers are not surrogate endpoints unless they are determined to be supported for use as such for a defined context of use through use of the biomarker evaluation framework and expert panel as described in Recommendations 1 and 2. The definition of “surrogate endpoint” is critical for clear communication and transparency in regulatory processes. Several definitions of surrogate endpoint have been used. Table 1-3 shows definitions that have appeared in regulations and other regulatory documents. Table 1-4 shows literature definitions. TABLE 1-3 Regulatory Definitions of Surrogate Endpoint Source Definition 57 FR 13234–13242 (1992)a A surrogate end point, or “marker,” is a laboratory measurement or physical sign that is used in therapeutic trials as a substitute for a clinically meaningful endpoint that is a direct measure of how a patient feels, functions or survives and is expected to predict the effect of the therapy. FDAMA (Food and Drug Administration Modernization Act) 1997 USC Section 504(b)(1) …a surrogate endpoint that is reasonably likely to predict clinical benefit. Title 21 – Food and Drugs 21 C.F.R. 314 Section 314.510b …a surrogate endpoint that is reasonablylikely, based on epidemiologic, therapeutic, pathophysiologic, or other evidence, to predict clinical benefit. Guidance for Industry: Evidence-based review system for the scientific evaluation of health claimsc Surrogate endpoints are risk biomarkers that have been shown to be valid predictors of disease risk and therefore may be used in place of clinical measurements of the onset of the disease in a clinical trial. SOURCES: a New drug, antibiotic and biological drug product regulations: accelerated approval. Proposed Rule. 57 Federal Register 13234–13242 (1992). b Food and Drug Modernization Act of 1997, 21 USC section 506(b)(1) (1997). Title 21—Food and Drugs, 21 CFR 314 Section 314.510 (2008) [http://frwebgate5.access.gpo.gov/cgi-bin/TEXTgate.cgi?WAIISdocID=026369143256+87+1+0&WAISaction=retrieve]. c http://www.cfsan.fda.gov/~dms/hclmgui6.html.
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease TABLE 1-4 Literature Definitions of Surrogate Endpoint Source Definition Biomarkers Definitions Working Group (2001) A biomarker that is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence. Guide to Clinical Trials (Spilker, 1991) The ideal surrogate endpoint is a disease marker that reflects what is happening with the underlying disease. The relationship between the marker and the true endpoint is important to establish. After this is done, the validity of data based on how the marker is affected by a medicine or other treatment can be translated into a valid statement about the disease and true endpoint. Prentice (1989)a A response variable for which a test of the null hypothesis of no relationship to the treatment groups under comparison is also a valid test of the corresponding null hypothesis based on the true endpoint. Temple (1995)a A surrogate endpoint of a clinical trial is a laboratory measurement or a physical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels, functions or survives. Changes induced by a therapy on a surrogate endpoint are expected to reflect changes in a clinically meaningful endpoint. Johnston (1999)a A surrogate outcome measure is simply one that is used in place of a clinical endpoint ... an adequate surrogate measure must not only correlate with the clinical endpoint, but it must be predictive of the clinical endpoint in the presence of the intervention under study. Baker et al. (2005)a A surrogate endpoint is defined as a measure or indicator of a biological process that is obtained sooner, at less cost or less invasively than a true endpoint of health outcome, and is used to make conclusions about the effect of an intervention on the true endpoint. Grimes and Schulz (2005)a A valid surrogate endpoint must both correlate with and accurately predict the outcome of interest. Gluud et al. (2007)a A surrogate outcome measure is a laboratory measurement, a physical sign, or any other intermediate substitute that is able to predict a treatment response on a clinically meaningful outcome measure. Pryseley et al. (2007)a A surrogate for a true endpoint is an endpoint that can be used in lieu of the true endpoint to assess treatment benefits. That is, the effect of the treatment on the surrogate endpoint should reliably predict the effect of the treatment on the true endpoint. Gobburu (2009); Lathia et al. (2009) A biomarker that is intended to substitute for a clinical endpoint. NOTE: a See also the Shi and Sargent (2009) compilation of these definitions.
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease There are a few common features in the overwhelming majority of the surrogate endpoint definitions. First, a surrogate endpoint is meant to substitute for a clinically meaningful endpoint. Second, the surrogate endpoint needs to predict change in those clinical outcomes given an intervention. The last definition in Table 1-4 appears to be for a proposed surrogate endpoint, not for one that has already been determined to satisfy the requirements of a true surrogate endpoint. The committee views this definition as being too inclusive to be accurate. This definition is not consistent with consensus and most regulatory definitions of surrogate endpoint. The last definition in Table 1-3 is the definition used by CFSAN for review of health claims that industry submits for inclusion in food labeling. The citation given in the guidance document is for Spilker’s Guide to Clinical Trials (shown in Table 1-4; 1991); however, the definition in the guidance document is not consistent with the one it cites. In Dr. Spilker’s more recent book, Guide to Drug Development: A Comprehensive Review and Assessment (2009), the Biomarkers Definitions Working Group definition is used. The CFSAN definition does not include a critical component of the definition of surrogate endpoints: the ability to predict clinical benefit or harm of an intervention based on a change in the surrogate endpoint. The use of the word “valid” in this definition is also ambiguous, as will be discussed below. Finally, the CFSAN definition accounts only for use of surrogate endpoints in clinical trials and does not allow for use in observational studies. The Biomarkers Definitions Working Group’s definition takes into account uses of surrogate endpoints in observational studies. There are a number of other important concepts to understand when considering surrogate endpoints. The Prentice criteria are succinctly summarized in two parts: correlation and capture. Under correlation, the surrogate endpoint must be statistically correlated to the clinical endpoint. In other words, the surrogate endpoint should have prognostic value relative to the clinical endpoint. Under capture, an intervention’s entire effect on the clinical endpoint should be explained by the intervention’s effect on the surrogate endpoint. In other words, the surrogate endpoint should account for all of an intervention’s effects; the surrogate endpoints should be a perfect proxy for the effect of an intervention on the recipient’s risk of important clinical outcomes (Desai et al., 2006; Prentice, 1989). The terms “clinical endpoint” and “true endpoint” are sometimes used interchangeably. The definition of clinical endpoint given in Box 1-1 is widely accepted and consistently used, while the term true endpoint is broader and ill defined. To some, only all-cause mortality is a true endpoint. In practice, however, a trial’s true endpoint is defined by the experimenters. It can be mortality due to the disease being studied, failure of the treatment (which can be defined in several ways), time to progres-
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease sion, or something else. Sometimes, a surrogate endpoint in one study can be the clinical endpoint in another study. Practically, the true endpoint is the endpoint for which a surrogate endpoint is sought. Myocardial infarction (MI) is an example. Because an MI in a person outside the hospital is detected from symptoms, it is a plausible clinical endpoint. However, it should be acknowledged that a significant element of the importance of MI derives from both the fact that it is a biomarker for risk of future events (death, heart failure) and that it requires objective biomarker measurements for the diagnosis. The term “validation” encompasses many different aspects of biomarker development. In the statistics literature, validation means what other fields term “qualification.” Validation and analytical validation are often used interchangeably, as are clinical validation and qualification. Clinical utility is often used interchangeably with utilization. In this report, the committee uses validation and analytical validation interchangeably, qualification but not clinical validation, and utilization but not clinical utility. Correct definition of the terms food, substance, disease, and drug are important for understanding FDA regulations. Food is defined as (1) articles used for food or drink for humans or other animals, (2) chewing gum, and (3) articles used for components of any such article.5 As was noted in the summary of this report, however, the committee has been more explicit in its definition: the term “food” is inclusive of foods consumed as part of meals and snacks, dietary supplements, and components contained in them (nutrients, other bioactive substances). A substance is “a specific food (tomato) or component of food (lycopene), whether in conventional food or dietary supplement form”6 (Trumbo and Ellwood, 2009). A disease or health-related condition is “Damage to an organ, part, structure, or system of the body such that it does not function properly (e.g., CHD), or a state of health leading to such dysfunctioning (e.g., hypertension)”7 (Trumbo and Ellwood, 2009). A drug is defined as “articles intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease” and “articles (other than food) intended to affect the structure or any function of the body of man or other animals”8 (FDA, 2002). The term “intervention” refers to any drug, device, biologic, behavioral modification, nutritional modification, lifestyle modification, or other treatment intended to improve health. 5 FDCA, Sec. 201(II)(f). 6 21 C.F.R. 101.14(a)(2). 7 21 C.F.R. 101.14(a)(5). 8 FDCA, Sec. 201(g)(1).
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Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease RELATED IOM WORK The committee views this report as building on and supporting the recommendations of several previous committees. In particular, the committee would like to reemphasize the recommendations of the report on Cancer Biomarkers (Box B-1) and the report on The Future of Drug Safety (Box B-2). The recommendations from both of these reports are included in Appendix B. Cancer Biomarkers grouped its recommendations into three categories: (1) methods, tools, and resources needed to discover and develop tools for cancer; (2) guidelines, standards, oversight, and incentives needed for biomarker development; and (3) methods and processes needed for clinical evaluation and adoption. Government agencies, academics, healthcare practitioners, industrial stakeholders, and the Institute of Medicine have been working to explore and implement changes that reflect the needs identified in the recommendations. As mentioned earlier, the current report was requested by the FDA as a path forward on recommendation 6 from the Cancer Biomarkers report. The recommendations from The Future of Drug Safety were grouped into categories: organizational culture, science and expertise, regulation, communication, and resources. Following the release of the report in 2007, the Food and Drug Administration Amendments Act was passed. It reauthorized a number of key pieces of legislation important for increasing drug safety and expanded FDA responsibilities and capabilities to respond to a number of The Future of Drug Safety report’s recommendations (FDA, 2009b). In 2009, the FDA published a table describing the significant progress made on implementation of the IOM recommendations (FDA, 2009c). FRAMEWORK OF THE REPORT The framework of the report follows the statement of task and the committee’s recommendations. Chapter 2 reviews previous biomarker and surrogate endpoint evaluation processes. Chapter 3 presents the committee’s recommended biomarker evaluation framework. Chapter 4 contains the case studies that exemplify use of the biomarker evaluation process. Finally, Chapter 5 describes data collection and data infrastructure needs to support the FDA’s work. REFERENCES Adams, C. P., and V. V. Brantner. 2006. Estimating the cost of new drug development: Is it really $802 million? Health Affairs 25(2):420–428. Baker, S. G., G. Izmirlian, and V. Kipnis. 2005. Resolving paradoxes involving surrogate end points. Journal of the Royal Statistical Society Series A 168(4):753–762.
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