Serum sIFNAR2 Biomarker Predicts Interferon Beta Response
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Solution Overview
Problem
Current methods lack a validated, minimally invasive, and easy-to-implement test to predict multiple sclerosis patients' response to interferon beta (IFNβ) treatment, with 30-50% of patients not responding adequately to the treatment.
Innovation Solution
Evaluating serum soluble Interferon Alpha/Beta Receptor 2 (sIFNAR2) levels as a biomarker using ELISA, comparing baseline levels to determine responsiveness before treatment initiation, and monitoring changes over time to classify patients as responders or non-responders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gene expression analysis methods are used to predict treatment response, then prediction capability is improved, but test complexity and cost increase
Solution Approach 1:
The patent extracts a specific soluble receptor (sIFNAR2) from the complex system of gene expression analysis. Instead of analyzing multiple genes and their expressions, the method focuses on measuring a single soluble protein marker in serum, thereby simplifying the test while maintaining prediction capability
Solution Approach 2:
The patent uses soluble IFNAR2 as an intermediary marker that reflects the biological response to IFNβ treatment. This intermediary protein serves as a measurable surrogate that connects treatment administration to clinical response without requiring direct gene expression analysis
2Measurement precision
If invasive tissue sampling is performed to obtain biomarkers, then measurement precision is improved, but patient comfort and ease of operation worsen
Solution Approach 1:
The patent utilizes serum, a body fluid that is easily obtainable through simple venipuncture, to measure sIFNAR2 levels. The serum naturally contains the soluble receptor, eliminating the need for invasive tissue sampling or complex sample preparation while providing sufficient material for accurate measurement
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively predicts treatment response by distinguishing between responders and non-responders based on baseline sIFNAR2 levels, with treatment increasing sIFNAR2 levels in non-responders to match those of responders, providing a clinical tool for selecting appropriate treatment.
Implementation Method 1
measuring slFNAR2 protein levels in a serum sample
Data Source
Figure 1~1B
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AI summary
A method for predicting or prognosticating the response of individuals with multiple sclerosis to treatment with IFNβ, kit or device, and uses.