Gene Expression Classifier for Anti-TNF Response Prediction
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Solution Overview
Problem
Current anti-TNF therapies face challenges in consistently identifying responder versus non-responder patients, leading to inconsistent response rates and increased risks, including serious infections and malignancies, due to the inability to predict which patients will benefit from the treatment, resulting in delayed proper treatment and significant healthcare costs.
Innovation Solution
The development of a method using gene expression response signatures and a human interactome map to classify patients as responders or non-responders by identifying specific gene expression patterns, allowing for targeted administration of anti-TNF therapy and reducing unnecessary treatment and side effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If anti-TNF therapy is administered to all patients with autoimmune disorders, then more patients may potentially benefit from treatment, but response rates become inconsistent and non-responder patients are exposed to unnecessary risks and side effects
Solution Approach 1:
The patent applies preliminary action by performing gene expression analysis and classifier-based prediction before administering anti-TNF therapy. This pre-screening process identifies responders vs non-responders in advance, allowing clinicians to prescribe anti-TNF therapy only to predicted responders, thereby ensuring consistent response rates while avoiding unnecessary treatment exposure for non-responders
2Speed
If anti-TNF therapy is administered without predictive tools, then treatment can be started immediately, but non-responder patients experience delayed proper treatment and increased healthcare costs
Solution Approach 1:
The classifier system performs preliminary prediction of treatment response before therapy initiation, eliminating the need for trial-and-error treatment approaches. By predicting responder status in advance through gene expression analysis, the system enables immediate prescription of effective therapy to responders while avoiding ineffective treatment of non-responders, thus eliminating treatment delays
3Measurement precision
If gene expression analysis is performed to predict treatment response, then response prediction accuracy improves, but diagnostic complexity and testing requirements increase
Solution Approach 1:
The patent extracts and focuses on a specific subset of genes with known associations with anti-TNF response (such as TNFRSF1A, TNFRSF1B, and other TNF pathway-related genes) rather than analyzing the entire genome. This targeted approach maintains high prediction accuracy while reducing diagnostic complexity by concentrating on biologically relevant markers
Data Source
AI summary
Methods and systems for administering therapy to subjects who have been determined to not display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received anti-TNF therapy.