Composite Biomarker Panel for Early SLE Risk Prediction
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Solution Overview
Problem
Current methods for identifying individuals at risk of transitioning to systemic lupus erythematosus (SLE) are not robust enough to predict disease onset effectively, leading to inadequate early intervention and increased morbidity and mortality.
Innovation Solution
A method involving the assessment of specific biomarkers in blood, serum, or urine samples, including innate and adaptive serum mediators, chemokines, soluble TNF superfamily members, inflammatory mediators, and SLE-associated autoantibodies, to generate a score indicating the likelihood of developing SLE, allowing for preclinical intervention with treatments like hydroxychloroquine, belimumab, or DMARDs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If ANA positivity is used to identify future SLE patients, then early detection capability is improved, but false positive rate increases leading to reduced prediction accuracy
Solution Approach 1:
The patent segments the single ANA biomarker into a multi-component biomarker panel including ANA, anti-dsDNA, anti-Smith, anti-Ro, anti-La, and anti-phospholipid antibodies. This segmentation allows each biomarker to contribute specific information, improving prediction accuracy while maintaining early detection capability through the composite risk score.
Solution Approach 2:
The patent creates a composite risk score by combining multiple biomarker measurements (ANA, anti-dsDNA, anti-Smith, anti-Ro, anti-La, anti-phospholipid antibodies) with clinical criteria. This composite approach integrates diverse information sources to achieve both early detection and high prediction accuracy, resolving the contradiction between sensitivity and specificity.
2Measurement precision
If multiple biomarkers are assessed to improve prediction accuracy, then measurement precision is improved, but device complexity and assessment difficulty increase
Solution Approach 1:
The patent develops a unified risk assessment tool that simultaneously evaluates multiple biomarkers (ANA, anti-dsDNA, anti-Smith, anti-Ro, anti-La, anti-phospholipid antibodies) and clinical criteria through a single composite risk score calculation. This multi-functional approach maintains high prediction accuracy while simplifying the assessment process into one integrated tool rather than separate evaluations.
Solution Approach 2:
The patent merges multiple separate biomarker assessments and clinical evaluations into a single composite risk score. By combining the measurement of six different autoantibodies with clinical criteria assessment, the invention creates one unified metric that improves prediction accuracy without requiring complex separate evaluation protocols.
3Reliability
If early intervention is implemented based on risk prediction, then patient outcomes are improved, but treatment costs and resource utilization increase
Solution Approach 1:
The patent enables preliminary identification of high-risk individuals through the composite risk score before clinical SLE manifests. This preliminary action allows targeted early intervention only for those with high predicted risk, improving patient outcomes through timely treatment while avoiding unnecessary resource utilization in low-risk individuals who would not benefit from intervention.
Solution Approach 2:
The patent applies early intervention resources locally and selectively to high-risk individuals identified by the composite risk score, rather than universally to all ANA-positive individuals. This localized approach concentrates resources on patients most likely to benefit, improving outcome reliability while optimizing resource utilization by avoiding treatment of low-risk individuals.
Data Source
AI summary
The present invention includes methods, systems, and kits, for identifying and modifying the treatment of a systemic lupus erythematosus (SLE) patient prior to the presence of autoantibodies, comprising: (a) obtaining a dataset representing protein expression level values for cytokines and molecules; (b) assessing the dataset for protein expression levels of at least one innate serum mediator; (c) assessing the dataset for protein expression levels of at least one adaptive serum mediator; and (d) determining the likelihood that the patient will develop SLE prior to the onset of autoantibodies when compared to a control.