C04371: Novel Diagnostic and Prognostic Multiple Sclerosis Protein BiomarkerNovelty:
A novel diagnostic and prognostic protein biomarker in Cerebral Spinal Fluid that is 100% specific for Multiple Sclerosis when compared to other neurologic disorders.
Value Proposition:
Current Multiple Sclerosis diagnosis is produced and confirmed strictly by clinical evaluation, without the use of precise protein biomarker identification. A highly specific protein biomarker for Multiple Sclerosis will allow clinicians to more quickly and precisely identify patients with the disease, potentially before clinically relevant symptoms arise. Other advantages include:
• Accurate and inexpensive way to identify Multiple Sclerosis in clinical setting.
• Early detection of Multiple Sclerosis can prevent significant neurodegeneration associated with the disease.
• Can be developed into prognostic marker in addition to diagnostic marker to aid in targeted therapies.
• First protein biomarker for detection of Multiple Sclerosis gives market advantage for product development.
Technical Details:
Johns Hopkins researchers have discovered a novel protein biomarker that is 100% specific to relapse-remitting Multiple Sclerosis conditions which may be a sensitive indicator of recent disease activity. A method that uses chromatographic surfaces to bind and identify proteins in complex mixtures known as Surface-Enhanced Laser Desorption/Ionization Time of Flight mass spectrometry (SELDI-TOF-MS) has uncovered disease -specific protein expression signatures in clinical samples. This technology now permits the high throughput proteomic screening of cerebral spinal fluid (CSF) for the identification of biomarkers specific for multiple sclerosis.
Looking for Partners:
To develop and commercialize the technology as a diagnostic kit for detection of Multiple Sclerosis biomarkers.
Stage of Development:
Pre-clinical
Data Availability:
Human data: Mass spectrometry data confirming link between biomarker and disease.
Publications/Associated Cases:
Not available at this time
