Personalized Medicine Decision Machine for Drug Combination Optimization
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Solution Overview
Problem
Current personalized medicine approaches often rely on empirical data and fail to optimize combinatorial therapies for multifactorial diseases, such as multi-genic diseases, due to limitations in considering molecular markers, drug interactions, and socio-economic factors.
Innovation Solution
A method that involves obtaining patient data with molecular biomarkers, standardizing and curating it, and using a decision-making machine to identify and optimize therapeutic options by querying clinical outcome databases and applying supervised-learning algorithms to predict efficacious and safe drug combinations, while considering drug-drug interactions and socio-economic factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If monotherapy is used to simplify treatment, then ease of operation is improved, but treatment efficacy deteriorates in multifactorial diseases
Solution Approach 1:
The patent segments the treatment approach by classifying molecular biomarkers into different functional effects (e.g., oncogenic, tumor suppressor, metabolic) and matching them with specific therapeutic agents. This segmentation allows complex multifactorial diseases to be treated through targeted combinations rather than simplified monotherapy, resolving the contradiction between treatment simplicity and efficacy.
Solution Approach 2:
The system dynamically generates personalized combination therapies based on each patient's specific molecular biomarker profile. Rather than a fixed monotherapy approach, the treatment regimen is dynamically adapted to the patient's unique genetic characteristics, thereby improving treatment efficacy while maintaining a systematic and manageable approach through automated algorithmic generation.
2Reliability
If combinatorial therapies are optimized considering multiple factors, then treatment efficacy is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal decision-making machine that integrates multiple functions: data standardization, biomarker classification, therapeutic option identification, and combination optimization. This multi-functional system handles diverse input data types and generates comprehensive treatment recommendations, improving efficacy while consolidating complexity into a single integrated platform rather than multiple separate systems.
Solution Approach 2:
The system introduces an intermediary layer of standardized biomarker classification and functional effect categorization between raw patient data and treatment recommendations. This intermediary structure organizes complex molecular data into manageable categories, enabling the system to process multiple factors without proportionally increasing overall system complexity.
3Measurement precision
If extensive patient data is analyzed for personalized treatment, then measurement precision is improved, but loss of time increases due to data processing
Solution Approach 1:
The patent implements preliminary action by pre-establishing classification frameworks for molecular biomarkers based on their functional effects. These classification schemas and decision algorithms are prepared in advance, allowing the system to rapidly categorize and process patient-specific biomarker data without performing complex analysis from scratch, thereby reducing processing time while maintaining precision.
Solution Approach 2:
The system transforms complex molecular biomarker data into standardized functional effect categories, changing the parameters from raw molecular measurements to classified functional groups. This parameter transformation simplifies subsequent processing and comparison operations, reducing the time required to analyze extensive patient data while preserving the essential diagnostic information.
Data Source
AI summary
In an aspect, a method includes obtaining patient data for a patient, the patient data including one or more molecular biomarkers specific to the patient, standardizing and curating the patient data, classifying the standardized and curated patient data including classifying the one or more molecular biomarkers by one or more of its functional effects, using the one or more functional effects for the one or more molecular biomarkers to identify a combination of available therapeutic options by targeting biomarkers with relevant functional effects based on the patient data, applying a learning method to optimize results for presentation to a user, and presenting optimized results to the user.