Biomarker definitions and their applications

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.

[1]  Peter Sandercock,et al.  Interpretation of the evidence for the efficacy and safety of statin therapy , 2016, The Lancet.

[2]  Michael E. Miller,et al.  Effects of intensive glucose lowering in type 2 diabetes. , 2008, The New England journal of medicine.

[3]  R. Califf,et al.  Biomarkers and Surrogate Endpoints: Developing Common Terminology and Definitions. , 2016, JAMA.

[4]  Barry S. Coller,et al.  Traversing the Valley of Death: A Guide to Assessing Prospects for Translational Success , 2009, Science Translational Medicine.

[5]  P. F. Kauff Group , 2000, Elegant Design.

[6]  T. Insel Digital Phenotyping: Technology for a New Science of Behavior. , 2017, JAMA.

[7]  Gary Gintant,et al.  Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. , 2014, American heart journal.

[8]  N. Cook Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction , 2007, Circulation.

[9]  Michael E. Miller,et al.  The Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes Study (ACCORD-MIND): rationale, design, and methods. , 2007, The American journal of cardiology.

[10]  C. Lam,et al.  Correlation of the New York Heart Association Classification and the 6‐Minute Walk Distance: A Systematic Review , 2015, Clinical cardiology.

[11]  Sidney C. Smith,et al.  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: A Report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents. , 2016, Journal of the American College of Cardiology.

[12]  Thomas R. Fleming,et al.  Surrogate Endpoints in Clinical Trials , 1996 .

[13]  R. Prentice Surrogate endpoints in clinical trials: definition and operational criteria. , 1989, Statistics in medicine.

[14]  F. Sundler,et al.  The mouse trap. , 1997, Trends in pharmacological sciences.

[15]  Joseph Loscalzo,et al.  Precision medicine in cardiology , 2016, Nature Reviews Cardiology.

[16]  Zane T. Macfarlane,et al.  Validation of the Instant Blood Pressure Smartphone App. , 2016, JAMA Internal Medicine.

[17]  Jackson T. Wright,et al.  A Randomized Trial of Intensive versus Standard Blood-Pressure Control. , 2016, The New England journal of medicine.

[18]  Michael J Pencina,et al.  Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models , 2012, Statistics in medicine.

[19]  Joseph Loscalzo,et al.  Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine , 2012, Wiley interdisciplinary reviews. Systems biology and medicine.