Anti-TNF Response Classifier for Autoimmune Treatment Selection
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Solution Overview
Problem
Current anti-TNF therapies for autoimmune diseases like rheumatoid arthritis exhibit inconsistent response rates, leading to delayed treatment and increased risks of side effects due to the inability to accurately predict responder and non-responder patients.
Innovation Solution
A classifier is developed to distinguish between responsive and non-responsive patients using gene expression differences, single nucleotide polymorphisms, and clinical characteristics, allowing for personalized treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If anti-TNF therapy is administered to all RA patients, then treatment coverage is maximized, but treatment effectiveness decreases due to inclusion of non-responders
Solution Approach 1:
The patient population is segmented into responder and non-responder groups using a classifier based on gene expression profiles. This allows selective administration of anti-TNF therapy only to patients predicted to respond, improving treatment effectiveness while maintaining broad coverage through stratified patient identification.
Solution Approach 2:
The patent changes the parameter of patient selection from clinical criteria alone to multi-parameter assessment including gene expression signatures, SNPs, and clinical characteristics. This enhanced parameter set enables more accurate prediction of treatment response, allowing differentiation between responders and non-responders.
2Loss of time
If anti-TNF therapy is administered without response prediction, then treatment initiation is simplified, but treatment delay increases for non-responders
Solution Approach 1:
The classifier is applied before treatment initiation to predict response in advance. By performing this classification preliminary action, the system identifies non-responders before they undergo unnecessary treatment delays, allowing alternative therapies to be initiated sooner for patients unlikely to benefit from anti-TNF therapy.
Solution Approach 2:
The response prediction classifier acts as an intermediary between patient assessment and treatment decision-making. It processes multiple input parameters (gene expression, SNPs, clinical data) and outputs treatment recommendations, mediating the complexity of treatment selection while reducing overall treatment delay.
3Object-affected harmful factors
If anti-TNF therapy is administered to all patients, then treatment accessibility is maximized, but harmful side effects increase due to unnecessary treatment
Solution Approach 1:
Patients are segmented into those likely to respond and those unlikely to respond based on their molecular and clinical profile. This segmentation enables selective treatment administration, reducing harmful side effects in non-responders while maintaining accessibility for the responder population through targeted identification.
Solution Approach 2:
The patent converts the previously harmful effect of treating all patients (including non-responders) into a benefit by using the same comprehensive data collection to identify and exclude non-responders. The comprehensive data gathering that initially seemed to increase complexity actually enables more precise, harm-reducing treatment selection.
Data Source
AI summary
Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides methods of treating subjects suffering from an autoimmune disorder, the method comprising: administering an anti-TNF therapy to subjects who have been determined to be responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy. For example, in some embodiments, the present disclosure provides methods of treating subjects suffering from an autoimmune disorder during therapeutic treatment, the method comprising: identifying responsive and non-responsive prior subjects over a time period beginning from the administering of the anti-TNF therapy.