Molecular diagnostics clinical utility strategy: a six-part framework.

The clinical utility of a molecular test rises proportional to a favorable regulatory risk/benefit assessment, and clinical utility is the driver of payer coverage decisions. Although a great deal has been written about clinical utility, debates still center on its 'definition.' We argue that the definition (an impact on clinical outcomes) is self-evident, and improved communications should focus on sequential steps in building and proving an adequate level of confidence for the diagnostic test's clinical value proposition. We propose a six-part framework to facilitate communications between test developers and health technology evaluators, relevant to both regulatory and payer decisions.

Evidence of clinical utility: an unmet need in molecular diagnostics for patients with cancer.

This article defines and describes best practices for the academic and business community to generate evidence of clinical utility for cancer molecular diagnostic assays. Beyond analytical and clinical validation, successful demonstration of clinical utility involves developing sufficient evidence to demonstrate that a diagnostic test results in an improvement in patient outcomes. This discussion is complementary to theoretical frameworks described in previously published guidance and literature reports by the U.S. Food and Drug Administration, Centers for Disease Control and Prevention, Institute of Medicine, and Center for Medical Technology Policy, among others. These reports are comprehensive and specifically clarify appropriate clinical use, adoption, and payer reimbursement for assay manufacturers, as well as Clinical Laboratory Improvement Amendments-certified laboratories, including those that develop assays (laboratory developed tests). Practical criteria and steps for establishing clinical utility are crucial to subsequent decisions for reimbursement without which high-performing molecular diagnostics will have limited availability to patients with cancer and fail to translate scientific advances into high-quality and cost-effective cancer care. See all articles in this CCR Focus section, "The Precision Medicine Conundrum: Approaches to Companion Diagnostic Co-development."

Regulation, reimbursement, and the long road of implementation of personalized medicine--a perspective from the United States.

There is undisputed evidence that personalized medicine, that is, a more precise assessment of which medical intervention might best serve an individual patient on the basis of novel technology, such as molecular profiling, can have a significant impact on clinical outcomes. The field, however, is still new, and the demonstration of improved effectiveness compared with standard of care comes at a cost. How can we be sure that personalized medicine indeed provides a measurable clinical benefit, that we will be able to afford it, and that we can provide adequate access? The risk-benefit evaluation that accompanies each medical decision requires not only good clinical data but also an assessment of cost and infrastructure needed to provide access to technology. Several examples from the last decade illustrate which types of personalized medicines and diagnostic tests are easily being taken up in clinical practice and which types are more difficult to introduce. And as regulators and payers in the United States and elsewhere are taking on personalized medicine, an interesting convergence can be observed: better, more complete information for both approval and coverage decisions could be gained from a coordination of regulatory and reimbursement questions. Health economics and outcomes research (HEOR) emerges as an approach that can satisfy both needs. Although HEOR represents a well-established approach to demonstrate the effectiveness of interventions in many areas of medical practice, few HEOR studies exist in the field of personalized medicine today. It is reasonable to expect that this will change over the next few years.

Physician Awareness and Utilization of Food and Drug Administration (FDA)-Approved Labeling for Pharmacogenomic Testing Information.

We surveyed 10,303 United States physicians on where they obtain pharmacogenomic testing information. Thirty-nine percent indicated that they obtained this from drug labeling. Factors positively associated with this response included older age, postgraduate instruction, using other information sources, regulatory approval/ recommendation of testing, reliance on labeling for information, and perception that patients have benefited from testing. Physicians use pharmacogenomic testing information from drug labeling, highlighting the importance of labeling information that is conducive to practice application.

On rat poison and human medicines: personalizing warfarin therapy.

Teaching old dogs new tricks is difficult, but lessons learned from such efforts can be invaluable. Warfarin is an old drug, difficult to administer and a leading cause of drug-related mortality and hospitalizations. New genetic tests for optimizing warfarin therapy have not been adopted. The debate over precise clinical utility and cost-effectiveness of these tests misses more important points of building a better, cheaper, and more efficient infrastructure to measure the true real-world impact of personalized medicine. However, this same debate about how, when, and where such testing is appropriate has been invaluable to the field of personalized medicine: progress beyond science, in policy, regulations, and logistics can be highlighted along the path to safer and more efficacious warfarin therapy.

Considerations for safety pharmacogenetics in clinical practice.

The focus of treating an individual patient is the identification of the individual's specific needs. The measurement of the patient's characteristics, such as blood pressure or body temperature, and also the measurement of biomarkers, such as cholesterol or hemoglobin A1C is part of the patient's health assessment. The deeper the insights into the phenotypic and molecular characteristics of the patient, the better we are positioned to treat a patient. Increasingly, this assessment includes testing for certain pharmacologically relevant genetic variations (pharmacogenetics). Evaluating how the patient's genetic makeup combined with the patient's exposure to environmental influences could impact disease and treatment decisions is becoming the cornerstone of personalized medicine. However, we often use such assessments for finding the most 'effective' treatment, but we might not always be as rigorous in our assessment of potential safety risks. This is particularly apparent when looking at how safety risks are communicated. Often this information is only available as general, population-based statements and a small amount of information is available to evaluate whether or not an individual patient is at risk. Although pharmacogenetic tests that can help to assess whether an individual patient's personal risk exist (safety pharmacogenetics), they are not always performed.

The future of direct-to-consumer clinical genetic tests.

In light of the meeting of the US Food and Drug Administration (FDA) in March 2011 to discuss the regulation of clinical direct-to-consumer (DTC) genetic tests, we have invited five experts to consider the best means of overseeing the ordering and interpretation of these tests. Should these tests be regulated? If so, who, if anyone, should communicate results to consumers?

Impact of proton pump inhibitors on the effectiveness of clopidogrel after coronary stent placement: the clopidogrel Medco outcomes study.

STUDY OBJECTIVE: To investigate the potential impact of proton pump inhibitors (PPIs) on the effectiveness of clopidogrel in preventing recurrent ischemic events after percutaneous coronary intervention (PCI) with stent placement. DESIGN: Population-based, retrospective cohort study. DATA SOURCE: National medical and pharmacy benefit claims database comprising approximately 19 million members. PATIENTS: A total of 16,690 patients who had undergone PCI with stent placement and who were highly adherent to clopidogrel therapy alone (9862 patients) or to clopidogrel with a PPI (6828 patients) between October 1, 2005, and September 30, 2006. MEASUREMENTS AND MAIN RESULTS: The primary end point was the occurrence of a major adverse cardiovascular event during the 12 months after stent placement. These events were defined as hospitalization for a cerebrovascular event (stroke or transient ischemic attack), an acute coronary syndrome (myocardial infarction or unstable angina), coronary revascularization (PCI or coronary artery bypass graft), or cardiovascular death. A composite event rate was compared between patients who received clopidogrel alone and those who received concomitant clopidogrel-PPI therapy. Baseline differences in covariates were adjusted by using Cox proportional hazards models. In the 9862 patients receiving clopidogrel alone, 1766 (17.9%) experienced a major adverse cardiovascular event compared with 1710 patients (25.0%) who received concomitant clopidogrel-PPI therapy (adjusted hazard ratio 1.51, 95% confidence interval 1.39-1.64, p<0.0001). Similar associations of increased risk were observed for each PPI studied (omeprazole, esomeprazole, pantoprazole, and lansoprazole). CONCLUSION: Concomitant use of a PPI and clopidogrel compared with clopidogrel alone was associated with a higher rate of major adverse cardiovascular events within 1 year after coronary stent placement.

Real-world clinical effectiveness, regulatory transparency and payer coverage: three ingredients for translating pharmacogenomics into clinical practice.

The past decade of pharmacogenomics was driven by the sequencing of the human genome to create ever denser maps of genetic variations for studying the diversity across individuals. Today, genotyping technology is available at a fraction of the cost of what it was 10 years ago and many pharmacogenomic variations have been studied in detail. Still, we are only starting to gain an understanding of how pharmacogenomic-guided drug therapy affects clinical outcomes: real-world studies that demonstrate the clinical effectiveness and address the economic implications of pharmacogenomics are needed to help decide when and how to implement pharmacogenomics in clinical practice, how to regulate pharmacogenomic testing and how the healthcare system will integrate this new science into an environment of rapidly increasing cost.

Back to the future: why randomized controlled trials cannot be the answer to pharmacogenomics and personalized medicine.

Randomized controlled trials are the gold standard for determining the efficacy of therapeutic interventions. However, medical practice has not evolved around the concept of randomized trials, but around the idea of careful observations, (anecdotal) case studies and the evaluation of retrospective data. Interventions discovered by these means and taken forward into clinical practice became standard practice as they continued to be superior when compared with prior or alternative types of treatment. Personalized medicine refers to an approach of clinical practice where a particular treatment is not chosen based on the 'average patient', but on characteristics of an individual patient, for example, a genetic profile that may vary from one patient to another, and therefore, allows to 'personalize' the treatment to a patient's individual needs. While the call for prospective randomized controlled trials to assess the effective use of such measurement may make sense in some cases, it is, when applied without distinction, hindering the implementation of personalized medicine. Important evidence for the validity and clinical effectiveness of using biomarkers, for example, a patient's genetic profile, can be gained from alternative approaches, including case-control and cohort studies, and retrospective analyses of data. Hence, we need to re-focus on approaches that are neither new nor unproven, but have been ignored over the last few decades.

4th US FDA-Drug Information Association pharmacogenomics workshop, held 10-12 December, 2007.

The 4th US FDA/Industry workshop, in a series on Pharmacogenomics, was on 'Biomarkers and Pharmacogenomics in Drug Development and Regulatory Decision Making' and was held on December 10-12, 2007 in Bethesda, MD, USA, with clear objectives to continue the dialogue that began in 2002 for enabling the use of biomarkers and pharmacogenomics in drug development and regulatory decision-making. This brief commentary will highlight the major topics and outcomes discussed at this meeting that was jointly sponsored by the FDA, The Pharmacogenomics Working Group (PWG), The Pharmaceutical Research and Manufacturers of America (PhRMA), The Biotechnology Industry Organization (BIO) and The Drug Information Association (DIA).

Payer perspectives on pharmacogenomics testing and drug development.

A series of questions about hypothetical drugs and pharmacogenomic tests was posed to a panel of representatives from the health plan, government and employer sectors in order to elicit suggestions for input on data or study design considerations important for coverage determination. The panel suggested seven areas for drug developers to strongly consider. These areas were to include comparative information on new tests versus usual care, assess the negative predictive value of new tests, measure and report on cost offsets, balance relative risk improvement with absolute risk, consider the policy implications of the products or tests, report percentage responders in addition to group mean improvements, and to include specific pharmacogenomic information in US FDA approved labels. The panel was generally enthusiastic about the promise of the field to improve drug selection or dosing.

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.

BACKGROUND: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. RESULTS: Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. CONCLUSION: We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Pharmacogenomic biomarker information in drug labels approved by the United States food and drug administration: prevalence of related drug use.

STUDY OBJECTIVES: To review the labels of United States Food and Drug Administration (FDA)-approved drugs to identify those that contain pharmacogenomic biomarker information, and to collect prevalence information on the use of those drugs for which pharmacogenomic information is included in the drug labeling. DESIGN: Retrospective analysis. DATA SOURCES: The Physicians' Desk Reference Web site, Drugs@FDA Web site, and manufacturers' Web sites were used to identify drug labels containing pharmacogenomic information, and the prescription claims database of a large pharmacy benefits manager (insuring > 55 million individuals in the United States) was used to obtain drug utilization data. MEASUREMENTS AND MAIN RESULTS: Pharmacogenomic biomarkers were defined, FDA-approved drug labels containing this information were identified, and utilization of these drugs was determined. Of 1200 drug labels reviewed for the years 1945-2005, 121 drug labels contained pharmacogenomic information based on a key word search and follow-up screening. Of those, 69 labels referred to human genomic biomarkers, and 52 referred to microbial genomic biomarkers. Of the labels referring to human biomarkers, 43 (62%) pertained to polymorphisms in cytochrome P450 (CYP) enzyme metabolism, with CYP2D6 being most common. Of 36.1 million patients whose prescriptions were processed by a large pharmacy benefits manager in 2006, about 8.8 million (24.3%) received one or more drugs with human genomic biomarker information in the drug label. CONCLUSION: Nearly one fourth of all outpatients received one or more drugs that have pharmacogenomic information in the label for that drug. The incorporation and appropriate use of pharmacogenomic information in drug labels should be tested for its ability to improve drug use and safety in the United States.

Strategic paths for biomarker qualification.

Biomarkers may be qualified using different qualification processes. A passive approach for qualification has been to accept the end of discussions in the scientific literature as an indication that a biomarker has been accepted. An active approach to qualification requires development of a comprehensive process by which a consensus may be reached about the qualification of a biomarker. Active strategies for qualification include those associated with context-independent as well as context-dependent qualifications.

Qualification of biomarkers for drug development in organ transplantation.

The drug development process is dependent upon having established end points for measuring drug efficacy and adverse effects. New drug development in organ transplantation suffers from having end points which are either outdated or which do not serve the purpose of addressing the current critical drug therapy problems. Numerous biomarkers have been examined in organ transplantation, but almost all would be classified as exploratory for drug development purposes. Some of the possible pathways out of this dilemma include investigator- or consortium-initiated research that would qualify the biomarkers as either probable or known valid biomarkers, help in identification of new end points in transplantation and their associated biomarkers, co-development of a new biomarker and drug for transplantation and the use of new clinical trial design methods which facilitate enriched or stratified transplant patient populations. With new biomarkers and new study design methodologies for drug development, improvement in the drug development process for transplantation is a real possibility that the transplant clinical and research community can help to bring about.

Integration and use of biomarkers in drug development, regulation and clinical practice: a US regulatory perspective.

The US FDA encourages the integration of biomarkers in drug development and their appropriate use in clinical practice. It is believed that this approach will help alleviate stagnation and foster innovation in the development of new medical products, and, ultimately, lead to more personalized medicine. To facilitate the use of biomarkers in drug development and clinical practice, the FDA organized workshops, issued guidances, established a voluntary submission process, developed online educational tools and, most importantly, strives to ensure the integration of this information into drug labels, for example, via the update of existing labels, or the inclusion of appropriate language in new drug labels. A pilot process has been set up to qualify novel biomarkers that are not associated with specific drug products, but are of more common use (e.g., biomarkers for drug safety). In addition, the FDA has initiated the creation of various consortia that are working towards the identification and characterization of exploratory biomarkers in order to qualify them for a specific use.

Considerations for a business model for the effective integration of novel biomarkers into drug development.

It is 10 years since the introduction of trastuzumab into the US market, and we are still waiting for a validation of the business case for biomarker-driven drug development. While many reasons for the lack of duplication of this model may exist, the need for accelerated innovation in drug development paired with the opportunity of integrating biomarker-driven research into drug development programs may lead to new and creative ways of fostering the cooperation between drug developers and test manufacturers. The rapid increase in knowledge about biomarkers and our understanding of disease and disease mechanisms open unprecedented prospects to make not only better, more informed decisions regarding patient care, but also strategic decisions during drug development. This requires that a biomarker strategy becomes an integral part of (early) drug development and that new, innovative paths are tried towards a model that combines the scientific approach with an economically feasible implementation strategy. Collaborative research, the use of new communication tools, the exploration of alternative ways to position a product in the market, and other considerations are part of such a strategy. This perspective article illustrates the current landscape and takes a look at some of these new ways for more effectively integrating biomarkers into drug development.

Implementing the U.S. FDA guidance on pharmacogenomic data submissions.

The FDA Guidance for Industry: Pharmacogenomics Data Submissions was issued in 2005. This guidance document covers a broad area associated with how and when to submit genomic data to the FDA. Additional tasks associated with genomic data submissions include the implementation of genomic data submissions; the process for qualification of exploratory biomarkers into valid biomarkers; and technical recommendations for the generation and submission of genomic data to the FDA. These tasks have been addressed throughout the past 2 years by a number of initiatives. These initiatives have included the development of the Interdisciplinary Pharmacogenomics Review Group for review of pharmacogenomic data submissions, the pilot process for qualification of biomarkers, and the concept paper on recommendations for the generation and submission of genomic data. These initiatives have contributed to the effective implementation of the Pharmacogenomics Guidance at the FDA.

An integrated bioinformatics infrastructure essential for advancing pharmacogenomics and personalized medicine in the context of the FDA's Critical Path Initiative.

Pharmacogenomics (PGx) is identified in the FDA Critical Path document as a major opportunity for advancing medical product development and personalized medicine. An integrated bioinformatics infrastructure for use in FDA data review is crucial to realize the benefits of PGx for public health. We have developed an integrated bioinformatics tool, called ArrayTrack, for managing, analyzing and interpreting genomic and other biomarker data (e.g. proteomic and metabolomic data). ArrayTrack is a highly flexible and robust software platform, which allows evolving with technological advances and changing user needs. ArrayTrack is used in the routine review of genomic data submitted to the FDA; here, three hypothetical examples of its use in the Voluntary eXploratory Data Submission (VXDS) program are illustrated.: