Stroke is the third leading cause of death and is a leading cause of serious, long-term disability in the United States. A new or recurrent stroke is suffered by ~ 700,000 Americans each year. An estimated 57.9 billion dollars will be spent to cover the stroke-related medical treatment and disability care for these individuals in 2006 alone. Stroke diagnosis can be difficult due to a wide variety of symptoms that are often shared by other conditions. Although extensive research has been conducted on ischemic stroke, there remains no laboratory- based method of diagnosing the condition. Computed tomography (CT) imaging is one common method used to evaluate stoke patients, however, it is not a reliable method for detecting clots. Drug therapy for acute ischemic stroke is only effective if administered within three hours of the onset of symptoms. Only 3-5% of patients reach the hospital in enough time. Thus, improved diagnostic methods are needed. A number of research groups are performing detailed studies to evaluate the expression of individual biomarkers in ischemic stroke for use as a diagnostic tool. However, the standard method for measuring plasma or serum levels of cytokines, chemokines or other biomarkers is to measure them one at a time using Enzyme-Linked Immunosorbent Assay (ELISA). One-at-a-time assessment of each putative biomarker incurs considerable time, cost and sample volume. Clearly, no single molecular marker, or small group of markers, will be able to accurately classify individuals at highest risk. The ability to systematically identify protein profiles, predict risk of clinical events, evaluate therapeutic response, and define underlying mechanisms is thereby limited severely. The recent development of bead-based multiplex immunoassays provides an efficient approach for performing a rapid assessment of large numbers of plasma protein antigens. Rules-Based Medicine (RBM) has extended this approach to perform Multi-Analyte Profiles (MAP) of blood proteins using very small sample volumes (10- 20 ?L). This technology is well suited for screening large numbers of markers in parallel to identify protein profiles associated with ischemic stroke. RBM, in collaboration with Charles River Laboratories (CRL), proposes to identify biomarker patterns associated with ischemic stroke using stroke-prone rats as a model. It is expected that the physiological criteria obtained from the program may be used to better define the pathological mechanisms of the disease, and to identify disease biomarkers of ischemic stroke that may be applied to humans. Stroke accounted for 1 of every 15 deaths in the United States in 2003. About 50% of these deaths occurred out of hospital. The identification of novel biomarker patterns of individuals in the presymptomatic stages of ischemic stroke, as well as individuals at high risk for having an ischemic stroke, will provide a more rapid diagnosis, allow for improved management of the condition, and will provide a framework for developing and evaluating new treatments. Stroke is the third leading cause of death and is a leading cause of serious, long-term disability in the United States. The current clinical approach to diagnosis is at the onset of symptoms, which can vary greatly, and the outcome can be dire if not diagnosed in time, or if treated improperly. The identification of novel biomarker patterns of individuals in the presymptomatic stages of stroke, as well as individuals at high risk for having a stroke, would be of great public health benefit by providing a more rapid diagnosis, allowing for improved management of the condition, and will provide a framework for developing and evaluating new treatments. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43NS059215-01
Application #
7273124
Study Section
Special Emphasis Panel (ZRG1-BDCN-A (11))
Program Officer
Jacobs, Tom P
Project Start
2007-08-01
Project End
2008-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
1
Fiscal Year
2007
Total Cost
$99,605
Indirect Cost
Name
Rules-Based Medicine, Inc.
Department
Type
DUNS #
114417327
City
Austin
State
TX
Country
United States
Zip Code
78759