The Opportunities and Challenges of Developing ... - ATS Journals

4 downloads 21 Views 689KB Size Report
Mar 21, 2007 - Schuster, M.D., Washington University School of Medicine, 660 South Euclid. Avenue, St. Louis ... proof-of-efficacy (PoE) clinical studies. (D).
Pulmonary Perspective The Opportunities and Challenges of Developing Imaging Biomarkers to Study Lung Function and Disease Daniel P. Schuster1 1

Department of Internal Medicine and The Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri

Recent advances in imaging offer exciting opportunities to develop and validate lung-specific biomarkers as valuable adjuncts to diagnosis, tests of treatment efficacy, and/or treatment monitoring. State-of-the-art structural, functional, and molecular imaging methods allow the lungs to be visualized noninvasively in vivo at submillimeter and subsecond spatial and temporal scales. However, the development and validation of imaging biomarkers present some special challenges, including the following: equipment evaluation, procedure standardization, data regarding reproducibility and replication, interrater variability, the production and measurement of reference standards, sensitivity to interventions or disease progression, intersubject variance, choice of image reconstruction and segmentation algorithms, automated versus observer-dependent image analysis, data acquisition during conditions of standardized lung volume, whether a reliable association can be demonstrated between the imaging biomarker and a clinical endpoint, and whether its use will have a favorable cost-effective impact on drug development or disease management. Establishing such performance characteristics, especially for single investigators at single institutions, can be daunting if not impossible for costly biomarkers such as imaging. Therefore, to take full advantage of the opportunities presented by state-of-the-art imaging methods, new approaches to analytic and clinical validation must be developed in collaboration with industry, foundation, and federal funding agencies. Keywords: drug discovery; drug development; positron emission tomography; magnetic resonance imaging; X-ray computed tomography

America’s investment in biomedical research justifies a timely translation of basic science discovery into clinical medical practice. Thus, it is both disappointing and surprising that, despite tremendous advances in discovery, the number of new medical therapies making it to clinical practice has actually slowed recently (1). One strategy to accelerate progress is to use newly developed biomarkers, to phenotype patients more accurately, to establish a diagnosis more definitively, to manage disease, and to predict prognosis. Biomarkers that support the efficacy or identify the toxicity of new therapeutic interventions can also have a dramatic effect on “go–no go” decisions for development, on the costs associated with development, and on the time to complete development. Until recently, most biomarkers have been measures of physiology or assays of key biochemicals in blood or other bodily

(Received in original form March 21, 2007; accepted in final form May 2, 2007 ) Correspondence and requests for reprints should be addressed to Daniel P. Schuster, M.D., Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110. E-mail: [email protected] Am J Respir Crit Care Med Vol 176. pp 224–230, 2007 Originally Published in Press as DOI: 10.1164/rccm.200703-462PP on May 3, 2007 Internet address: www.atsjournals.org

fluids. Recent advances in imaging, however, offer exciting opportunities to develop and validate new organ-specific biomarkers. Interestingly, most reviews of pulmonary biomarkers have failed to even mention imaging as a strategy for biomarker development (2–4). Indeed, the development and validation of imaging biomarkers present some special challenges. Although imaging also plays an increasingly important role in animal studies and preclinical drug development, the primary focus of this perspective is the opportunities and challenges associated with developing and validating in vivo imaging biomarkers to measure biological phenomena in the lungs of humans.

DEFINITIONS AND CONCEPTS The most widely accepted definition of a biomarker, the result of a National Institutes of Health/U.S. Food and Drug Administration working group, is that it is any “characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (5, 6). Biomarkers may be classified in different ways, but one useful scheme (particularly relevant to drug development) is to divide biomarkers into those that show “proof of mechanism,” “proof of principle,” or “proof of efficacy,” depending on whether they demonstrate an interaction with an intended target, a modification to a pathway downstream from the target, or an effect on the intended disease, respectively (Figure 1). Biomarkers of the last type are sometimes also called “surrogate endpoints” or “clinical correlates” (7). Surrogate endpoints are meant to replace clinical endpoints (Figure 2). Clinical endpoints are specifically those biomarkers that “reflect how a patient feels, functions, or survives” (5). Clinical endpoints (e.g., mortality, length of intensive care unit stay, number of disease exacerbations per year) have intrinsic value to patients. However, clinical endpoints can be difficult to quantify and may not develop until long after exposure to a risk factor or drug. For these reasons, surrogate endpoints can be valuable when the markers change in some predictable way earlier in the disease process than can clinical endpoints (allowing clinical trials of shorter duration) and/or they can be measured with greater precision than clinical endpoints (allowing clinical trials to be planned with a smaller “n”). Although surrogates such as cholesterol reduction, glucose control, viral DNA levels, and so forth are well known (8), imaging biomarkers have also been used as surrogates, in studies of rheumatoid arthritis and of cancer, among others (9). They have not, however, been used in registration trials for new pulmonary drugs. In some cases, “surrogate endpoint clusters” (10) may be better predictors of clinical outcome than single surrogates, if for no other reason than disease is rarely the result of a single factor that can be entirely encapsulated by one biomarker. These

Pulmonary Perspective

225

Figure 1. Path to biomarker discovery, development, validation, and use. (A ) Conventional path for drug development. (B ) Analogous path for biomarker development and validation. (C ) Utility of biomarkers (BM) in proof-of-mechanism (PoM), proof-of-principle (PoP), and proof-of-efficacy (PoE) clinical studies. (D ) Illustration of why a biomarker may not qualify as a surrogate endpoint (if intervention, e.g., Rx3, affects a path not represented by the biomarker). (E ) Potential role of imaging as a biomarker of antiinflammatory effect, using cystic fibrosis for illustration. CFTR ⫽ cystic fibrosis transmembrane conductance regulator.

clusters might include both imaging and nonimaging surrogates (see below). No funding or regulatory agency has specifically defined what constitutes an “imaging biomarker.” Smith and colleagues defined these to be “anatomic, physiologic, biochemical, or molecular parameters detectable with imaging methods [be they microscopic, ex vivo, or in vivo] used to establish the presence or severity of disease [i.e., either diagnosis or prognosis]” (11). In reality, imaging biomarkers are simply biomarkers for which imaging is the quantitative instrument of measurement.

DEVELOPING AND VALIDATING BIOMARKERS Imaging biomarkers, like biomarkers in general, can be developed and validated by a process not unlike that used to develop

Figure 2. Biomarker subsets. Surrogate endpoints are designed, if properly validated, to replace clinical endpoints. Some in vivo imaging biomarkers can, if properly validated, serve as surrogate endpoints.

and test new drugs (Figure 1). Eventually, the biomarker may be qualified by the U.S. Food and Drug Administration (FDA) as a surrogate endpoint if it is “reasonably likely, based on epidemiologic, therapeutic, pathophysiologic, or other evidence” that such an effect predicts clinical benefit or on the basis of an effect on a clinical endpoint other than survival or irreversible morbidity” (cited from Subpart H, Sec. 313.510, of the Code of Federal Regulations, accessible via the FDA website, www.fda. gov). (Other useful definitions regarding biomarker validation are given in Reference 6). Prentice proposed a reasonable set of criteria to establish surrogacy status for a new biomarker (12): (1 ) the treatment must modify the surrogate, (2 ) the treatment must modify the clinical endpoint, (3 ) the surrogate and clinical endpoint must be significantly correlated, and (4 ) the effect of treatment on the clinical endpoint should disappear when statistically adjusting for its effect on the surrogate. These criteria, however, are not universally accepted (13). Both analytic and clinical validation (the latter referred to as “qualification” by the FDA (see http://www.fda.gov/ohrms/ dockets/ac/04/slides/2004–4079s2.htm [accessed February 17, 2007]) (14) are critical steps in biomarker development (Table 1, Figure 3). Issues to be resolved during analytic validation include the following: equipment evaluation, procedure standardization, quality assurance protocols, reproducibility and replication, interrater variability, the production and measurement of reference standards (which, in the case of imaging, would include the use of standardized “phantoms” and reference imaging sets), sensitivity to interventions or disease progression, and intersubject variance. Analytic validation of imaging biomarkers also requires attention to the choice of image reconstruction algorithm, image segmentation algorithm (to isolate classes of lung structures—airways, vessels, parenchyma, etc.— from one another), automated versus observer-dependent image analysis, and, specifically for the lungs, data acquisition during conditions of standardized lung volume (15). The principal issue to be resolved during clinical validation is whether a reliable

226

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 176 2007

TABLE 1. VALIDATION CRITERIA FOR PROOF-OF-PRINCIPLE AND PROOF-OF-EFFICACY BIOMARKERS (SURROGATE ENDPOINTS) Analytic validation • Method standardization • Performance characteristics (reproducibility, sensitivity, specificity, accuracy, limits of detection) • Dependency on observer evaluation (intra- and interobserver variation) • Storage for future replication or improved analysis of results Clinical validation • Relationship to disease mechanism • Correlation with disease diagnosis or prognosis* • Correlation with disease severity* • Responsiveness to treatment Other factors to be considered • Relative invasiveness • Potential for sampling bias (region-of-interest analyses) • Potential for serial monitoring • Cost • Regulatory requirements (e.g., imaging requiring radiation exposure) * Relevant specifically to proof-of-efficacy biomarkers.

(preferably causal) association can be demonstrated between the biomarker and the clinical endpoint (criterion 3 above), and whether its use as a surrogate will have a favorable cost-effective impact on drug development or disease management. Relatively few imaging biomarkers have achieved surrogacy status. Reasons include lack of method standardization, inadequate information about performance (sensitivity, specificity,

Figure 3. Biomarker validation. The end result should be a set of data (the darkened area) that adequately addresses analytic validity, clinical validity, and practicality.

reproducibility, etc.), and inadequate or nonexistent validation against clinically meaningful endpoints. In general, bioassays that are performed on human tissue specimens for diagnostic or disease management reasons are performed by laboratories certified under the Clinical Laboratory Improvement Amendments (CLIA) using standards developed by various nationally recognized institutes or committees (6). Similarly, a lung-specific biomarker, like pulmonary function testing, has been standardized by the American Thoracic Society and European Respiratory Society (information available at http://www.thoracic.org/ sections/publications/statements/index.html [last accessed March 17, 2007]). No comparable set of standards exists for imaging biomarkers, however. The importance of standardizing imaging methodology is crucial, and institutes such as the National Cancer Institute are beginning to address this issue for potential imaging surrogates in cancer (16). Although similar issues have been raised regarding X-ray CT to obtain imaging biomarkers for CF and chronic obstructive pulmonary disease (COPD) (15), a set of agreed-upon standards do not yet exist. Despite this, characterization of the issues and agreement about how to resolve them are much farther along in the case of CT than is the case with other forms of imaging (e.g., magnetic resonance [MR] or radionuclide imaging). Dynamic imaging (the acquisition of repeat images over brief periods of time) can be used to derive a variety of functional measurements relevant to lung physiology or pathophysiology (e.g., ventilation, blood flow, or transfer rate of some tracer from one tissue compartment to another) by using a mathematical model to analyze changes in the regional concentration of some source of contrast (e.g., a contrast agent in the case of CT or radioactivity in the case of radionuclide imaging), ultimately changing the set of images into a single map representing the regional distribution of the physiologic process being studied. It is this transformed parametric image that is then analyzed and interpreted biologically. These mathematical models must incorporate such factors as delivery of the contrast agent to tissue, blood concentration of contrast agent, tissue uptake, metabolism, recirculation of metabolized and unmetabolized tracer, and the heterogeneity of tissues within the resolution volume of the image (the “voxel”) (17). The physiologic data finally analyzed from such an “imaging” study will only be as accurate as the mathematical model used to calculate them—the same imaging data may yield different results when analyzed mathematically with different models. Assessing the sensitivity and accuracy of these models is an important, but often overlooked, aspect of the technical validation of an imaging method. Obtaining imaging data for validation, especially against clinical endpoints, is not easy. For nonimaging biomarkers, one common strategy is to obtain tissue/blood samples during the course of a clinical trial that is otherwise being conducted anyway with clinical endpoints. These tissue banks then allow associations between a potential new biomarker and a clinical endpoint to be determined in either case-control (retrospective) or cohort (prospective) studies. Later, promising surrogates can be tested during interim evaluations in subsequent clinical trials to determine whether the candidate biomarker indeed reliably predicts clinical outcome. This strategy, however, is not possible for candidate imaging biomarkers because the imaging studies have to be obtained prospectively, often leading to difficult choices about technique, platform, and cost before the performance characteristics of the various imaging alternatives are known. As with any other biomarker (Figure 1), a candidate imaging biomarker may fail clinical validation for the following reasons: it does not measure a biological phenomena that is actually in the causal pathway of the disease process, there are several causal pathways but the intervention only affects the pathway

Pulmonary Perspective

227

represented by the biomarker, the biomarker is not in the pathway of the intervention’s effect or is insensitive to the intervention’s effect, or the intervention has mechanisms of action independent of the disease process (7, 18, 19).

IN VIVO IMAGING PLATFORMS AND MODALITIES Imaging is performed for different reasons. Anatomic imaging is used to display structure (e.g., airway diameter) or to make measurements related to structure (e.g., lung volumes, such as functional residual capacity). Functional imaging usually depends on data obtained over finite time periods to measure dynamic physiologic processes such as ventilation, perfusion, or pulmonary vascular permeability. Molecular imaging represents a relatively new set of techniques used to “directly or indirectly monitor and record the spatiotemporal distribution of molecular or cellular processes [e.g., enzyme activity] for biochemical, biologic, diagnostic, or therapeutic applications” (20). Different imaging platforms (CT, MR, and radionuclide imaging [e.g., positron emission tomography (PET)]) can be used for these different purposes (anatomic, functional, or molecular imaging) (Table 2). Increasingly, images from more than one modality are superimposed on one another, allowing structure– function and function–function relationships to be studied on a regional basis. This new capability is useful to determine with certainty the tissue compartment from which functional or molecular imaging signals originate. In addition, however, multimodality imaging raises the possibility of developing imaging “signatures”—that is, the combination of imaging biomarkers that characterize a particular disease. Such signatures, with or without additional clinical information, may be a novel way to phenotype patients for genetic association studies, for pharmacogenetic studies, or to identify subsets of patients in whom to test new drugs.

SOME EXAMPLES OF LUNG IMAGING BIOMARKERS IN DEVELOPMENT Proof-of-Mechanism Imaging Biomarkers

To demonstrate that a new drug interacts with its intended target, the drug must be appropriately labeled and an assay performed on a tissue sample from the relevant organ. In humans, obtaining tissue for this purpose can obviously be a challenge, so imaging offers an attractive noninvasive solution (11, 21, 22, 23). At present, drugs are usually radiolabeled for in vivo imaging of drug–target interactions in humans. With PET in particular, it is possible to label the drug without any change to the drug’s chemical structure; thus, the pharmacokinetics of the labeled drug will be identical to the unlabeled molecule. Indeed, in a single imaging session, it may be possible to establish targeting, whole-body organ distribution (especially valuable to determine

if there is uptake, unexpectedly, in nontarget organs, a result that could raise drug safety concerns), and routes and rates of drug clearance (pharmacokinetics). Because drugs may be delivered to the lungs by inhalation, deposition of the drug along the airways or its distribution throughout the lung parenchyma will depend greatly on the method of delivery (i.e., the issue is not just whether the drug interacts with its target but whether it ever even reaches its target). Here, imaging can again be of special value, as demonstrated, for instance, in asthma (24, 25). Proof-of-Principle Imaging Biomarkers

Most imaging biomarkers fall into this class because any study that seeks to determine whether a drug affects a disease pathway distal to its interaction with its intended target requires some readout of that effect. The ability to serially “biopsy” lung tissue noninvasively provides a strong incentive to incorporate imaging into studies that seek to determine whether the drug or intervention is having its intended effect in humans. Structural imaging. High-resolution CT can be used to evaluate structural changes to the airways in patients with COPD, asthma, and CF. Airway wall thickness—one example of various related CT-derived measures of small airways dimensions—is often increased in these diseases, leading some to speculate that it is an imaging biomarker of inflammation-induced airway wall remodeling (26). In COPD, these imaging biomarkers correlate with histologic measurements in post–surgically resected lung tissue and with various measurements of airway function (27, 28). In patients with CF (29), a more comprehensive cataloging of CT abnormalities (including such features as mucus plugging and air trapping in addition to changes in airway dimensions) has led to the development of several CT-based scoring systems (30) (in essence, a biomarker “cluster”). CT has also been used to measure lung parenchymal density as an imaging biomarker of tissue injury (e.g., decreased density in emphysema and increased density in the acute respiratory distress syndrome) (31). Diffusion hyperpolarized 3He-MR imaging provides a new strategy for evaluating lung microstructure noninvasively, in this case without the need for ionizing radiation (32). In normal lungs, the apparent diffusivity of 3He gas (i.e., the Brownian motion of 3He within small, distal, primarily acinar, airways and airspaces) is restricted relative to its behavior in large airways such as the trachea. With lung destruction (as in emphysema), this apparent diffusivity (quantified as the apparent diffusivity coefficient) increases, providing a quantitative measure of airspace enlargement. Functional imaging. All three major imaging platforms (CT, MR, and PET) have been used to study regional lung ventilation and perfusion. In asthma, imaging studies reveal surprisingly

TABLE 2. EXAMPLES OF LUNG BIOMARKERS MEASURABLE WITH THE MAJOR IMAGING PLATFORMS Anatomic

Functional

Molecular

X-ray CT

Airway wall thickness (27, 30) Parenchymal density (31)

Ventilation and perfusion (49)

MRI

Apparent diffusivity coefficient (32)

Ventilation and perfusion (33)

Pulmonary edema (51)

Ventilation and perfusion (52) vascular permeability (53)

Pulmonary edema (54) FDG uptake (39) PK11195 uptake (55)

PET

Definition of abbreviations: CT ⫽ computed tomography; FDG ⫽ [18F]fluorodeoxyglucose; MRI ⫽ magnetic resonance imaging; PET ⫽ positron emission tomography. Reference numbers are in parentheses.

228

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 176 2007

large subsegmental and even segmental ventilation defects (33, 34)—surprising because they appear even in asymptomatic patients and because they are heterogeneously dispersed despite the conventional assumption that asthma is a disease that diffusely involves small airways. These defects appear to be somewhat more common in patients with severe disease (33). Likewise, each of the major imaging platforms can be used to measure pulmonary perfusion (35–37). (Pulmonary perfusion here refers to microvascular blood flow and is distinct from angiography, which shows blood flow through large conducting vessels). These methods have been used to study such phenomena as hypoxic pulmonary vasoconstriction, and the effects of positive end-expiratory pressure, posture during mechanical ventilation, and bronchoconstriction on regional pulmonary perfusion. Molecular imaging of inflammation. [18F]fluorodeoxyglucose 18 ([ F]FDG), the most widely used PET tracer in clinical practice, is a glucose analog that cannot be further metabolized after phosphorylation by hexokinase within cells. Because [18F]FDGphosphate also cannot exit from cells, the radioactivity remains trapped. As [18F]FDG accumulates, the concentration of radioactivity builds, eventually reaching a point at which it can be detected and quantified by an appropriately calibrated PET camera. Although increased cellular uptake of [18F]FDG reflects cell processes requiring increased glucose metabolism, increases in tissue uptake of [18F]FDG can reflect both increased cellular metabolism and/or the influx of new metabolically active (primarily inflammatory) cells. Primarily as a result of the latter, numerous studies document that FDG-PET imaging can detect various inflammatory processes in humans, including those involving the lungs (38, 39). In general, acute increases in whole lung uptake of [18F]FDG reflect tissue invasion by activated neutrophils (although acute diseases such as eosinophilic pneumonia can be an exception [40]). Despite the correlation with neutrophil influx, parenchymal cells may also contribute to the imaging signal (41). FDG-PET imaging can detect and quantify the focal inflammatory response induced by either segmental allergen challenge (a model of atopic asthma) (42) or segmental instillation of lowdose endotoxin (a model of neutrophilic airway inflammation) (43). These studies may provide a basis for early testing of antiinflammatory drugs (39). In this regard, the cellular nonspecificity driving the FDG-PET imaging signal may or may not be valuable. For instance, FDG-PET imaging of a new antiinflammatory agent might not be adequately sensitive if a study was designed to specifically test the drug’s impact on a particular component driving the imaging signal (e.g., neutrophil influx). On the other hand, the drug’s mechanism of action might simultaneously cause favorable decreases in both neutrophil influx and activation of resident parenchymal cells. In this case, the nonspecificity of the FDG-PET imaging signal would increase test sensitivity. Other PET tracers in development may provide additional information about the inflammatory response. For instance, [11C]PK11195, a compound that binds to peripheral benzodiazepine receptors that are expressed on activated macrophages, might be useful for imaging macrophage responses in lung disease (44). Proof-of-Efficacy Imaging Biomarkers: The Challenges of Establishing Surrogacy

To date, no imaging biomarker can be considered validated (i.e., FDA “qualified”) as a surrogate for a clinical endpoint in trials related to lung disease. A study by Robinson and colleagues (45) illustrates many of the issues discussed in this perspective as to why this is so. In their study, 25 patients with CF were studied at a single academic medical center 12 months after initiating a trial of dornase alfa. Seven CT-derived imaging bio-

markers (using a standardized imaging protocol) and two functional biomarkers (the FEV1 and the forced expiratory flow during the middle half of a forced vital capacity [FEF25–75]) were evaluated before and after treatment. A small effect in preserving the FEV1 (which is an FDA-qualified biomarker for clinical trials in this patient population) in the dornase alfa treatment group compared with the placebo group was not statistically significant. A similarly favorable effect on the more sensitive FEF25–75 measure of airflow did not quite reach statistical significance (p ⫽ 0.073). Either alone or in combination, none of the various imaging biomarkers was more effective than the FEF25–75 in predicting a treatment effect of dornase alfa. However, in a post hoc analysis, a composite score combining both pulmonary function and imaging showed a highly significant difference (p ⫽ 0.007) between the treatment and placebo groups. Thus, this surrogate “cluster” (10) might indeed achieve the goal of enabling smaller, and therefore more efficient but still robust, evaluations of therapeutic interventions, but only if it can be assumed that this cluster can be substituted for the apparently less sensitive FEV1, or more importantly, for an actual clinical endpoint (e.g., mortality or clinical functionality). Furthermore, other aspects of clinical validation (Table 1), such as the relationship of this biomarker cluster to disease mechanism and its correlation with prognosis or disease severity, remain unknown. Such clinical validation, however, would require the resources of a prospectively conducted multicenter trial. Validation of other biomarker clusters that include imaging (46, 47) face similar hurdles. Indeed, for the goal of establishing surrogacy, one might imagine a kind of hierarchy of imaging biomarkers: in the natural history of any disease, changes in molecular imaging (indicating changes in the expression of a specific molecule) should precede changes in functional imaging, which in turn should precede anatomic changes detected by structural imaging—all before expression of or changes in the clinical disease become evident. As a consequence, it may be difficult to validate changes in molecular imaging as a surrogate endpoint, being “farther” from the downstream expression of clinical disease (Figure 1). The value of a molecular imaging biomarker could still be highly significant in understanding disease pathogenesis or in early drug development (proof of principle, Figure 1). For instance, to develop new drugs for inflammatory lung diseases such as CF, COPD, or asthma, one might envision using FDGPET imaging to first test the potential antiinflammatory effects of a new drug in normal volunteers after inducing focal inflammation with segmental instillation of low-dose endotoxin or allergen (42, 43). A favorable result could be followed by testing the drug in a small group of actual patients using FDG-PET imaging as the proof-of-principle biomarker. Such a study could be combined with functional imaging of regional ventilation to determine if antiinflammatory effects (as judged by the results with FDG-PET) could be correlated with improvements in airway function. Alternatively, functional imaging might be used to identify (“phenotype”) subsets of patients for such studies. Finally, a study of moderate duration could be designed to determine whether the new drug had important effects on structural imaging endpoints because these, either alone or in combination with other clinical biomarkers, might be most likely to correlate with—and thus be surrogates for—important clinical endpoints (45–47).

RECOMMENDATIONS None of the promising imaging biomarkers just discussed is yet routinely used in proof-of-principle or proof-of-efficacy studies because the required performance data (Table 1) have not been

Pulmonary Perspective

acquired. Reasons include cost, complexity, the need to obtain the raw imaging data prospectively, and a low priority for funding such work. But without such information, the promise of these technological breakthroughs will not be fully realized. To meet this challenge, new strategies to achieve analytic and clinical validation are needed, preferably involving a multidisciplinary effort that includes imaging scientists representing each of the different imaging platforms, biologists and physiologists who can apply the new imaging tools to answer relevant biological and clinical questions, and representatives from both industry and federal funding agencies. Protocol and method standardization for both image acquisition and image analysis must be a top priority. Such standardization would include the following: methods manuals; calibration against common imaging phantoms; the use of standardized reference imaging sets (48); observer-independent image analysis tools (49); and mechanisms for archiving, sharing, and transporting imaging data to external sites for later or independent review (50). Professional societies and/or federal governmental agencies could play a valuable role by organizing symposia or workshops for the purpose of defining and implementing these standards, preferably with industry input. Another priority should be to acquire sufficient patient imaging data to adequately characterize test performance (Table 1). This goal cannot be achieved by individual investigators at single institutions. It will only be possible in the context of networks or consortia that agree to work together. To fund such an effort, one could imagine a mechanism not unlike the contracts that currently fund groups such as the ARDS Network. Thus, groups would be invited to submit proposals for the development and validation of lung imaging biomarkers (perhaps with enough lead time to encourage pilot data to be gathered through an NIH R21 grant mechanism), the best would be chosen by peer review, and those groups that were chosen would meet to subsequently decide which proposals should be implemented first. Finally, journals should consider demanding that the details of image acquisition and analysis be described in manuscripts they accept for publication (these details, of course, could be archived online rather than in the main text). An FDA whitepaper, in discussing how to accelerate the development of useful new therapies (1), notes that “how soon these new [imaging] tools will be available for use will depend on the effort invested in developing them specifically for this purpose.” That is indeed the challenge; but the opportunity is a substantial improvement in time and proof of efficacy, and in the translation of discovery to clinical practice. Conflict of Interest Statement : D.P.S. currently has a grant from Pfizer, Inc., to study FDG-PET imaging as a biomarker of lung inflammation. Acknowledgment : The author thanks Dr. David Gierada for his review of the manuscript.

References 1. U.S. Food and Drug Administration. March 2004 report: innovation or stagnation: challenge and opportunity on the critical path. Available from: http://www.fda.gov/oc/initiatives/criticalpath/ (accessed February 10, 2007). 2. Barnes P, Chowdhury B, Kharitonov S, Magnussen H, Page C, Postma D, Saetta M. Pulmonary biomarkers in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006;174:6–14. 3. Frank J, Parsons P, Matthay M. Pathogenetic significance of biological markers of ventilator-associated lung injury in experimental and clinical studies. Chest 2006;130:1906–1914. 4. Kharitonov S, Barnes P. Exhaled biomarkers. Chest 2006;130:1541–1546. 5. Downing G; Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints. Clin Pharmacol Therap 2001;69:89–95.

229 6. Lee J, Devanarayan V, Barrett U, Weiner R, Allinson J, Fountain S, Keller S, Weinryb I, Green M, Duan L. Fit-for-purpose method development and validation for successful biomarker measurement. Pharmaceut Res 2006;23:312–328. 7. Frank R, Hargreaves R. Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2003;2:566–580. 8. Lesko L, Atkinson AJ. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu Rev Pharmacol Toxicol 2001;41:347–366. 9. Wang Y-XJ. Medical imaging in pharmaceutical clinical trials: what radiologists should know. Clin Radiol 2005;60:1051–1057. 10. Colburn W. Biomarkers in drug discovery and development: from target identification through drug marketing. J Clin Pharmacol 2003;43:329– 341. 11. Smith JJ, Sorensen AG, Thrall JH. Biomarkers in imaging: realizing radiology’s future. Radiology 2003;227:633–638. 12. Prentice R. Surrogate markers in clinical trials: definition and operational criteria. Stat Med 1989;8:431–440. 13. Alonso A, Molenberghs G, Geys H, Buyse M, Vangeneugden T. A unifying approach for surrogate marker validation based on Prentice’s criteria. Stat Med 2006;25:205–221. 14. Lee JW, Weiner RS, Sailstad JM, Bowsher RR, Knuth DW, O’Brien PJ, Fourcroy JL, Dixit R, Pandite L, Pietrusko RG, et al. Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development: a conference report. Pharmaceut Res 2005; 22:499–511. 15. Reilly J. Using computed tomographic scanning to advance understanding of chronic obstructive pulmonary disease. Proc Am Thorac Soc 2006;3:450–455. 16. Kelloff GJ, Hoffman JM, Johnson B, Scher HI, Siegel BA, Cheng EY, Cheson BD, O’Shaughnessy J, Guyton KZ, Mankoff DA, et al. Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development. Clin Cancer Res 2005;11:2785–2808. 17. Schuster DP. Positron emission tomography: theory and its application to the study of lung disease. Am Rev Respir Dis 1989;139:818–840. 18. Fleming TR, DeMets DL. Surrogate end points in clinical trials: are we being misled? Ann Intern Med 1996;125:605–613. 19. Fleming TR. Surrogate endpoints and FDA’s accelerated approval process. Health Aff 2005;24:67–78. 20. Thakur ML, Lentle BC, Society of Nuclear Medicine; Radiological Society of North America. Joint SNM/RSNA molecular imaging summit statement. J Nucl Med 2005;46:11N–13N, 42N. 21. Pien HH, Fischman AJ, Thrall JH, Sorensen AG. Using imaging biomarkers to accelerate drug development and clinical trials. Drug Discov Today 2005;10:259–266. 22. Chandra S, Muir C, Silva M, Carr S. Imaging biomarkers in drug development: an overview of opportunities and open issues. J Proteome Res 2005;4:1134–1137. 23. Passchier J, Gee A, Willemsen A, Vaalburg W, van Waarde A. Measuring drug-related receptor occupancy with positron emission tomography. Methods 2002;27:278–286. 24. Dolovich M, Labiris R. Imaging drug delivery and drug responses in the lung. Proc Am Thorac Soc 2004;1:329–337. 25. Newman SP. Can lung deposition data act as a surrogate for the clinical response to inhaled asthma drugs? Br J Clin Pharmacol 2000;49:529– 537. 26. Tiddens H, Silverman M, Bush A. The role of inflammation in airway disease: remodeling. Am J Respir Crit Care Med 2000;162:S7–S10. 27. de Jong PA, Muller NL, Pare PD, Coxson HO. Computed tomographic imaging of the airways: relationship to structure and function. Eur Respir J 2005;26:140–152. 28. Hasegawa M, Nasuhara Y, Onodera Y, Makita H, Nagai K, Fuke S, Ito Y, Betsuyaku T, Nishimura M. Airflow limitation and airway dimensions in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006;173:1309–1315. 29. Aziz ZA, Davies JC, Alton EW, Wells AU, Geddes DM, Hansell DM. Computed tomography and cystic fibrosis: promises and problems. Thorax 2007;62:181–186. 30. de Jong PA, Ottink MD, Robben SGF, Lequin MH, Hop WCJ, Hendriks JJE, Pare PD, Tiddens HAWM. Pulmonary disease assessment in cystic fibrosis: comparison of CT scoring systems and value of bronchial and arterial dimension measurements. Radiology 2004;231:434–439. 31. Rouby JJ, Puybasset L, Nieszkowska A, Lu Q. Acute respiratory distress syndrome: lessons from computed tomography of the whole lung. Crit Care Med 2003;31:S285–S295.

230

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 176 2007

32. Conradi MS, Yablonskiy DA, Woods JC, Gierada DS, Jacob RE, Chang YV, Choong CK, Sukstanskii AL, Tanoli T, Lefrak SS, et al. 3He diffusion MRI of the lung. Acad Radiol 2005;12:1406–1413. 33. de Lange EE, Altes TA, Patrie JT, Gaare JD, Knake JJ, Mugler JP III, Platts-Mills TA. Evaluation of asthma with hyperpolarized helium-3 MRI: correlation with clinical severity and spirometry. Chest 2006;130: 1055–1062. 34. Venegas JG, Winkler T, Musch G, Vidal Melo MF, Layfield D, Tgavalekos N, Fischman AJ, Callahan RJ, Bellani G, Harris RS. Selforganized patchiness in asthma as a prelude to catastrophic shifts. Nature 2005;434:777–782. 35. Levin DL, Hatabu H. MR evaluation of pulmonary blood flow. J Thorac Imaging 2004;19:241–249. 36. Musch G, Venegas JG. Positron emission tomography imaging of regional pulmonary perfusion and ventilation. Proc Am Thorac Soc 2005;2:522– 527. 37. Schuster DP, Anderson C, Kozlowski J, Lange N. Regional pulmonary perfusion in patients with acute pulmonary edema. J Nucl Med 2002;43: 863–870. 38. Zhuang H, Yu JQ, Alavi A. Applications of fluorodeoxyglucose-PET imaging in the detection of infection and inflammation and other benign disorders. Radiol Clin North Am 2005;43:121–134. 39. Chen DL, Schuster DP. Imaging pulmonary inflammation with positron emission tomography: a biomarker for drug development. Mol Pharm 2006;3:488–495. 40. Kim TJ, Lee KW, Kim HY, Lee JH, Kim EA, Kim SK, Kang KW. Simple pulmonary eosinophilia evaluated by means of FDG PET: the findings of 14 cases. Korean J Radiol 2005;6:208–213. 41. Zhou Z, Kozlowski J, Goodrich AL, Markman N, Chen DL, Schuster DP. Molecular imaging of lung glucose uptake after endotoxin in mice. Am J Physiol Lung Cell Mol Physiol 2005;289:L760–L768. 42. Taylor IK, Hill AA, Hayes M, Rhodes CG, O’Shaughnessy KM, O’Connor BJ, Jones HA, Hughes JM, Jones T, Pride NB, et al. Imaging allergen-invoked airway inflammation in atopic asthma with [18F]fluorodeoxyglucose and positron emission tomography. Lancet 1996; 347:937–940. 43. Chen DL, Rosenbluth DB, Mintun MA, Schuster DP. FDG-PET imaging of pulmonary inflammation in healthy volunteers after airway instillation of endotoxin. J Appl Physiol 2006;100:1602–1609.

44. Jones H, Cadwallader K, White J, Uddin M, Peters A, Chilvers E. Dissociation between respiratory burst activity and deoxyglucose uptake in human neutrophil granulocytes: implications for interpretation of (18)F-FDG PET images. J Nucl Med 2002;43:652–657. 45. Robinson TE, Leung AN, Northway WH, Blankenberg FG, Chan FP, Bloch DA, Holmes TH, Moss RB. Composite spirometric-computed tomography outcome measure in early cystic fibrosis lung disease. Am J Respir Crit Care Med 2003;168:588–593. 46. Brody AS, Sucharew H, Campbell JD, Millard SP, Molina PL, Klein JS, Quan J. Computed tomography correlates with pulmonary exacerbations in children with cystic fibrosis. Am J Respir Crit Care Med 2005; 172:1128–1132. 47. Martinez FJ, Foster G, Curtis JL, Criner G, Weinmann G, Fishman A, DeCamp MM, Benditt J, Sciurba F, Make B, et al., for the NETT Research Group. Predictors of mortality in patients with emphysema and severe airflow obstruction. Am J Respir Crit Care Med 2006;173: 1326–1334. 48. Rubenfeld GD, Caldwell E, Granton J, Hudson LD, Matthay MA. Interobserver variability in applying a radiographic definition for ARDS. Chest 1999;116:1347–1353. 49. Hoffman EA, Simon BA, McLennan G. A structural and functional assessment of the lung via multidetector-row computed tomography: phenotyping chronic obstructive pulmonary disease. Proc Am Thorac Soc 2006;3:519–532. 50. Clark K, Gierada D, Moore S, Maffitt D, Koppel P, Phillips S, Prior F. Creation of a CT image library for the lung screening study of the National Lung Screening Trial. J Digit Imaging 2007;20:23–31. 51. Kauczor HU, Kreitner K. MRI of the pulmonary parenchyma. Eur Radiol 1999;9:1755–1764. 52. Musch G, Venegas JG. Positron emission tomography imaging of regional lung function. Minerva Anestesiol 2006;72:363–367. 53. Schuster DP, Stark T, Stephenson J, Royal H. Detecting lung injury in patients with pulmonary edema. Intensive Care Med 2002;28:1246– 1253. 54. Harris RS, Schuster DP. Visualizing lung function with positron emission tomography. J Appl Physiol 2007;102:448–458. 55. Jones HA, Marino PS, Shakur BH, Morrell NW. In vivo assessment of lung inflammatory cell activity in patients with COPD and asthma. Eur Respir J 2003;21:567–573.