MSRC Classifier for TNF Therapy Response Prediction
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Solution Overview
Problem
Current approaches to determining patient response to TNF inhibitors for rheumatoid arthritis are inefficient, leading to prolonged trial-and-error treatment processes and potential disease progression due to inadequate response prediction.
Innovation Solution
A molecular signature response classifier (MSRC) test is developed to assess clinical characteristics using Monte Carlo simulation, integrating inter- and intra-individual variability to predict patient response to TNF therapies before administration, thereby reducing variability in clinical assessments and improving treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical characteristics are used to assess patient response to TNF inhibitors, then treatment decisions can be made, but variability in clinical assessments reduces measurement precision
Solution Approach 1:
The patent replaces subjective clinical assessment (mechanical/manual evaluation by practitioners) with a computational classifier system that processes clinical characteristics through Monte Carlo simulation. This substitution eliminates inter- and intra-practitioner variability while maintaining comprehensive evaluation of multiple clinical features, thereby improving measurement precision without proportionally increasing system complexity.
2Loss of time
If trial-and-error approach is used to determine patient response, then treatment options can be explored, but time is wasted on ineffective treatments
Solution Approach 1:
The patent performs preliminary assessment of patient response likelihood before initiating TNF inhibitor treatment by analyzing clinical characteristics through the classifier. This preliminary action identifies probable non-responders in advance, allowing clinicians to avoid ineffective treatments and select alternative therapies upfront, thereby reducing the time lost to trial-and-error while improving the reliability of treatment selection.
3Adaptability or versatility
If subjective clinical assessments are used, then practitioner judgment is applied, but inter- and intra-practitioner variability hinders consistent determination of therapy effectiveness
Solution Approach 1:
The patent transforms subjective clinical assessments into standardized quantitative parameters by inputting clinical characteristics into a classifier that processes them through Monte Carlo simulation. This parameter transformation maintains the adaptability of clinical evaluation while eliminating practitioner variability, as the same input parameters always produce consistent output probabilities regarding patient response likelihood.
Data Source
AI summary
Presented herein are systems and methods for validating and/or developing (e.g., training) a classifier that identifies a subject suffering from an autoimmune disease (e.g., rheumatoid arthritis, RA) as likely responsive or likely non-responsive to a therapy (e.g., an anti-TNF therapy) prior to any administration of the therapy to the subject.