Inter-organ cross talk prediction for disease state analysis
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Solution Overview
Problem
Current methods fail to effectively predict the presence of diseases in one organ based on the state of another organ or to predict the stage of a disease across multiple organs, and they struggle to assess the efficacy and side effects of test substances due to the complexity of inter-organ cross talk systems.
Innovation Solution
An apparatus and program that measure and analyze inter-organ cross talk indicators, such as RNA and metabolites, from one organ to predict the state of another organ and the stage of a disease, and to evaluate the effects of test substances by comparing patterns with standard data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional drug testing methods are used to evaluate drug effects in living organisms, then the complexity of the inter-organ cross talk system causes high dropout rates in clinical trials, but if simplified cell-based models are used, then the ability to predict actual disease states and drug effects in the whole body is reduced
Solution Approach 1:
The patent introduces an information processing system as an intermediary that bridges cell-based measurements and whole-body disease state prediction. The system uses inter-organ cross talk indicators (such as metabolite levels, gene expression patterns) as mediator variables to translate local cellular changes into predictions about distant organ states and overall disease progression, resolving the contradiction between simplified measurement and comprehensive prediction.
Solution Approach 2:
The patent replaces complex mechanical/physiological observation systems with information processing and computational analysis. Instead of directly observing whole-body physiological changes through complex clinical trials, the system substitutes computational models that process molecular-level data (metabolites, transcripts) to predict disease states, thereby reducing the effective complexity of the evaluation system while maintaining prediction accuracy.
2Loss of time
If comprehensive whole-body monitoring is performed to detect disease in all organs, then early detection capability is improved, but the cost and complexity of the diagnostic system increases significantly
Solution Approach 1:
The patent extracts specific inter-organ cross talk indicators from the complex web of physiological interactions between organs. By identifying and measuring only the key indicator molecules (such as specific metabolites or gene expression patterns) that mediate communication between organs, the system can detect disease states in distant organs without needing to monitor all physiological parameters throughout the body, thus reducing diagnostic complexity while maintaining early detection capability.
Solution Approach 2:
The patent performs preliminary analysis by measuring molecular indicators in easily accessible samples (such as blood or urine) that reflect the state of distant organs before clinical symptoms appear. This preliminary detection using inter-organ cross talk indicators allows early identification of disease risk in organs that have not yet manifested symptoms, reducing the time loss for detection without requiring complex imaging or invasive procedures for every organ.
3Productivity
If multiple drugs are tested simultaneously to identify effective treatments, then the efficiency of drug discovery is improved, but the complexity of evaluating drug interactions and side effects increases
Solution Approach 1:
The patent creates a universal information processing framework that can evaluate multiple drugs and their combinations through a single integrated system. The same inter-organ cross talk indicator measurements and computational models used for single-drug evaluation can be applied to multi-drug regimens, allowing simultaneous assessment of multiple treatments, their interactions, and side effects without requiring separate evaluation systems for each scenario.
Data Source
AI summary
An apparatus 1 comprises a subject data obtaining unit 11 for obtaining subject data M4 of an inter-organ cross talk indicator in each organ other than a specific organ, a pattern similarity calculation unit 12 for calculating, by comparing the subject data M4 with standard data 1 of the inter-organ cross talk indicator, similarity of patterns of the inter-organ cross talk indicators, and a prediction unit 13 for predicting the presence of a specific disease and/or the stage of the specific disease by using the similarity as a measure.