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      Biomarker definitions and their applications

      1 , 2 , 3
      Experimental Biology and Medicine
      SAGE Publications

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          Abstract

          Biomarkers are critical to the rational development of medical therapeutics, but significant confusion persists regarding fundamental definitions and concepts involved in their use in research and clinical practice, particularly in the fields of chronic disease and nutrition. Clarification of the definitions of different biomarkers and a better understanding of their appropriate application could result in substantial benefits. This review examines biomarker definitions recently established by the U.S. Food and Drug Administration and the National Institutes of Health as part of their joint Biomarkers, EndpointS, and other Tools (BEST) resource. These definitions are placed in context of their respective uses in patient care, clinical research, or therapeutic development. We explore the distinctions between biomarkers and clinical outcome assessments and discuss the specific definitions and applications of diagnostic, monitoring, pharmacodynamic/response, predictive, prognostic, safety, and susceptibility/risk biomarkers. We also explore the implications of current biomarker development trends, including complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies. Finally, we discuss the challenges and potential benefits of biomarker-driven predictive toxicology and systems pharmacology, the need to ensure quality and reproducibility of the science underlying biomarker development, and the importance of fostering collaboration across the entire ecosystem of medical product development. Impact statement Biomarkers are critical to the rational development of medical diagnostics and therapeutics, but significant confusion persists regarding fundamental definitions and concepts involved in their use in research and clinical practice. Clarification of the definitions of different biomarker classes and a better understanding of their appropriate application could yield substantial benefits. Biomarker definitions recently established in a joint FDA-NIH resource place different classes of biomarkers in the context of their respective uses in patient care, clinical research, or therapeutic development. Complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies, together with biomarker-driven predictive toxicology and systems pharmacology, are reshaping development of diagnostic and therapeutic technologies. An approach to biomarker development that prioritizes the quality and reproducibility of the science underlying biomarker development and incorporates collaborative regulatory science involving multiple disciplines will lead to rational, evidence-based biomarker development that keeps pace with scientific and clinical need.

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          Most cited references6

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          Use and misuse of the receiver operating characteristic curve in risk prediction.

          The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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            Surrogate end points in clinical trials: are we being misled?

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              2017 Focused Update of the 2016 ACC Expert Consensus Decision Pathway on the Role of Non-Statin Therapies for LDL-Cholesterol Lowering in the Management of Atherosclerotic Cardiovascular Disease Risk

              In 2016, the American College of Cardiology published the first expert consensus decision pathway (ECDP) on the role of non-statin therapies for low-density lipoprotein (LDL)-cholesterol lowering in the management of atherosclerotic cardiovascular disease (ASCVD) risk. Since the publication of that document, additional evidence and perspectives have emerged from randomized clinical trials and other sources, particularly considering the longer-term efficacy and safety of proprotein convertase subtilisin/kexin 9 (PCSK9) inhibitors in secondary prevention of ASCVD. Most notably, the FOURIER (Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk) trial and SPIRE-1 and -2 (Studies of PCSK9 Inhibition and the Reduction of Vascular Events), assessing evolocumab and bococizumab, respectively, have published final results of cardiovascular outcomes trials in patients with clinical ASCVD and in a smaller number of high-risk primary prevention patients. In addition, further evidence on the types of patients most likely to benefit from the use of ezetimibe in addition to statin therapy after acute coronary syndrome has been published. Based on results from these important analyses, the ECDP writing committee judged that it would be desirable to provide a focused update to help guide clinicians more clearly on decision making regarding the use of ezetimibe and PCSK9 inhibitors in patients with clinical ASCVD with or without comorbidities. In the following summary table, changes from the 2016 ECDP to the 2017 ECDP Focused Update are highlighted, and a brief rationale is provided. The content of the full document has been changed accordingly, with more extensive and detailed guidance regarding decision making provided both in the text and in the updated algorithms. Revised recommendations are provided for patients with clinical ASCVD with or without comorbidities on statin therapy for secondary prevention. The ECDP writing committee judged that these new data did not warrant changes to the decision pathways and algorithms regarding the use of ezetimibe or PCSK9 inhibitors in primary prevention patients with LDL-C <190 mg/dL with or without diabetes mellitus or patients without ASCVD and LDL-C ≥190 mg/dL not due to secondary causes. Based on feedback and further deliberation, the ECDP writing committee down-graded recommendations regarding bile acid sequestrant use, recommending bile acid sequestrants only as optional secondary agents for consideration in patients intolerant to ezetimibe. For clarification, the writing committee has also included new information on diagnostic categories of heterozygous and homozygous familial hypercholesterolemia, based on clinical criteria with and without genetic testing. Other changes to the original document were kept to a minimum to provide consistent guidance to clinicians, unless there was a compelling reason or new evidence, in which case justification is provided.
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                Author and article information

                Journal
                Experimental Biology and Medicine
                Exp Biol Med (Maywood)
                SAGE Publications
                1535-3702
                1535-3699
                November 06 2017
                February 2018
                February 06 2018
                February 2018
                : 243
                : 3
                : 213-221
                Affiliations
                [1 ]School of Medicine, Duke University, Durham, NC 27710, USA
                [2 ]Verily Life Sciences (Alphabet), South San Francisco, CA 94043, USA
                [3 ]Department of Medicine, Stanford University, Stanford, CA 94305, USA
                Article
                10.1177/1535370217750088
                5813875
                29405771
                7facdec0-c946-4936-9c50-bb2811edbcf5
                © 2018

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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