Personalized Therapy Targeting via Gene Expression Reversal
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Solution Overview
Problem
Current therapeutic approaches for complex diseases like autoimmune diseases and cancers often rely on reactive methods that are costly, risky, and delayed, failing to accurately predict patient response due to reliance on clinical characteristics rather than molecular insights.
Innovation Solution
A method using machine learning algorithms to identify disease gene expression signatures and potential therapeutic targets by reversing gene expression patterns to resemble healthy profiles, incorporating biological networks to predict therapy efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a reactive approach (trial-and-error) is used to identify treatment, then therapy options can be explored, but treatment time is delayed and adverse side effects increase
Solution Approach 1:
The patent performs preliminary analysis of clinical characteristics and gene expression profiles before initiating therapy. By pre-identifying patients likely to respond to specific treatments based on molecular signatures, the system eliminates the need for trial-and-error approaches, thereby reducing treatment time while maintaining therapeutic effectiveness.
Solution Approach 2:
The patent incorporates feedback mechanisms by continuously monitoring patient response to therapy and adjusting treatment plans accordingly. This real-time feedback allows for rapid optimization of therapy selection, reducing the time lost to ineffective treatments while adapting to individual patient responses.
2Ease of operation
If clinical characteristics are used to assess therapy response, then assessment is simple, but measurement precision of true response is limited
Solution Approach 1:
The patent introduces gene expression profiles and molecular signatures as intermediary markers between clinical characteristics and true therapy response. These molecular intermediaries provide more precise measurement of therapeutic effect by directly reflecting biological changes at the molecular level, thereby improving measurement precision while maintaining operational feasibility through standardized assays.
3Device complexity
If a single biomarker is used for therapy selection, then the approach is simple, but reliability of treatment prediction is insufficient for complex diseases
Solution Approach 1:
The patent employs composite molecular signatures composed of multiple gene expression markers rather than single biomarkers. By combining multiple genetic indicators into an integrated predictive model, the system achieves higher reliability in treatment prediction for complex diseases while managing complexity through computational algorithms that process the multi-marker data efficiently.
4Adaptability or versatility
If multiple therapies are attempted to treat complex diseases, then treatment coverage is improved, but adverse side effects and disease progression risk increase
Solution Approach 1:
The patent performs preliminary stratification of patients into subgroups based on molecular profiles before treatment initiation. This pre-categorization ensures that each patient receives the most appropriate first-line therapy, avoiding unnecessary exposure to multiple treatments and their associated side effects, while still maintaining comprehensive treatment coverage through targeted subgroup-specific recommendations.
Data Source
AI summary
Described are methods and systems for identifying a target for therapy and treating a subject that exhibits a disease gene expression signature, comprising identifying and administering a therapy determined to revert a disease gene expression signature in a subject suffering from a disease, disorder, or condition toward a non-diseased expression signature (e.g., disease gene expression signature of a non-diseased subject).