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Diagnostic use of blood-biomarkers (Spontaneous changes in biomarkers…
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Generality Biomarkers
Biomarker definitions and their applications, Robert M Califf
- The basic definition of a biomarker is deceptively simple: “A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention.” This broad Definition encompasses therapeutic interventions and can be derived from molecular, histologic, radiographic, or physiologic characteristics.
- biomarkers should be distinct from direct measures of how a Person feels, functions, or survives—a category of measure known as a clinical outcome assessment (COA).
Diagnostic biomarkers: A diagnostic biomarker detects or confirms the presence of a disease or condition of interest, or identifies an individual with a subtype of the disease. Such biomarkers may be used not only to identify people with a disease, but to redefine the classification of the disease. One goal is to define a method for validation that assures that the biomarker can be measured reliably, precisely, and repeatably at a low cost.Monitoring biomarkers: When a biomarker can be measured serially to assess the status of a disease or medical condition for evidence of exposure to a medical product or environmental agent, or to detect an effect of a medical product or biological agent it is a monitoring biomarker. When blood pressure is treated or low-density lipoprotein (LDL) cholesterol-lowering drugs are used, blood pressure or LDL cholesterol levels are monitored. Similarly, when HIV infection is treated, CD4 counts are monitored. Monitoring biomarkers are also important in ensuring the safety of human research participants. For example, the safety threshold for drugs with possible liver toxicity is monitored through serial measurement of liver function tests, and cardiovascular events are measured through the use of serial troponins. Monitoring biomarkers are also useful for measuring pharmacodynamic effects, to detect early evidence of a therapeutic response, and to detect complications of a disease or therapy.Predictive biomarkers: A predictive biomarker is defined by the finding that the presence or change in the biomarker predicts an individual or group of individuals more likely to experience a favorable or unfavorable effect from the exposure to a medical product or environmental agent. Proving that a biomarker is useful for this purpose requires a rigorous approach to clinical studies. Ideally, patients with or without the biomarker are randomized to one of two or more treatments (or a placebo comparator) and differences in outcome as function of treatment are significantly related to the difference in presence, absence, or level of the biomarkerPrognostic biomarkers A prognostic biomarker is used to identify the likelihood of a clinical event, disease recurrence, or disease progression in patients with a disease or medical condition of interest. Although this distinction is not uniformly accepted, the BEST working groups concluded that prognostic biomarkers should be differentiated from susceptibility/risk biomarkers, which deal with association with the transition from healthy state to disease. Furthermore, they are distinguished from predictive biomarkers, which identify factors associated with the effect of intervention or exposure. Prognostic biomarkers
Prognostic biomarkers should be differentiated from susceptibility/risk biomarkers, which deal with association with the transition from healthy state to disease. Furthermore, they are distinguished from predictive biomarkers, which identify factors associated with the effect of intervention or exposure. Prognostic biomarkers are associated with differential disease outcomes, but predictive biomarkers discriminate those who will respond or not respond to therapy.
Surrogates In fact, for a biomarker to qualify as a surrogate, the biomarker must not only be correlated with the Outcome but the change in the biomarker must “explain” the change, in the clinical outcome. The term “explains” invokes statistical inference, which can only be made with confidence if the observation is made in multiple therapies that all change the biomarker.
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