In-solution Bio-macromolecular Interaction Quantification
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Solution Overview
Problem
Current diagnostic techniques for protein-protein interactions are limited by binary output and inability to quantify biophysical properties, leading to insufficient nuanced analysis, especially under physiological conditions in complex media, resulting in false positives and negatives.
Innovation Solution
A system comprising a device for in-solution quantitative analysis of bio-macromolecular interactions, combined with a data store and machine learning algorithm, which processes personal and interaction data to provide clinically relevant, quantitative outputs, including biomarker presence and disease severity, enabling personalized treatment regimens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surface immobilisation techniques (ELISA, SPR) are used for protein detection, then sensitivity is improved, but false positives and false negatives occur due to unspecific interactions and Hook/Prozone effect
Solution Approach 1:
The patent inverts the conventional approach by moving from surface-based immobilisation to solution-phase interactions. Instead of immobilising one binding partner on a surface and detecting the other, both binding partners remain in solution, eliminating surface-related artifacts while maintaining detection capability through alternative signal generation methods.
Solution Approach 2:
The patent introduces an intermediary approach by using a detection system that does not rely on direct surface binding. The detection mechanism uses soluble reagents and signal amplification strategies that mediate the detection process without requiring immobilisation, thereby avoiding the Hook effect and non-specific surface interactions.
2Device complexity
If binary output diagnostic techniques are used, then simplicity is maintained, but nuanced biomedical data and complexity of the system are insufficiently captured
Solution Approach 1:
The patent transitions from binary (one-dimensional) output to multi-dimensional quantitative analysis. By measuring multiple biophysical parameters simultaneously (binding affinity, stoichiometry, kinetics) and representing them in a multi-dimensional parameter space, the system captures the complexity of biomedical systems while providing structured, analyzable data.
Solution Approach 2:
The patent changes the output from a single binary state to multiple continuous biophysical parameters. By quantifying binding affinity (Kd), stoichiometry (n), and kinetic rates, the system transforms qualitative presence/absence data into quantitative measurements that preserve nuanced information about molecular interactions.
3Loss of information
If in-solution quantitative analysis is performed to capture biophysical properties, then nuanced diagnostic capability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a universal platform that can measure multiple biophysical parameters using a single device configuration. The system uses a unified theoretical framework (law of mass action) and consistent methodology to extract multiple parameters from the same experimental data, reducing the need for multiple specialized instruments and simplifying the overall system architecture.
Solution Approach 2:
The patent implements feedback mechanisms where the measured biophysical parameters inform subsequent experimental design and data interpretation. The system uses the collected quantitative data to refine models, validate hypotheses, and guide further investigations, creating a closed-loop system that manages complexity through iterative learning and model refinement.
Data Source
AI summary
A system is provided for improving the quantitative analysis of a sample. The system comprises a device; a data store and processing circuitry configured to operate the system. The device is configured to perform quantitative analysis of bio-macromolecular interactions in solution on a fluid sample to provided quantitative analysis data. The data store stores: personal data relating to a plurality of individuals; and data relating to bio-macromolecular interactions. The processing circuitry is configured to access the data store and identify and retrieve data relevant to the sample; set the parameters under which the quantitative analysis of the sample is performed in the device in dependence upon said retrieved data; perform analysis using a general model to create a predicted result of the quantitative analysis from the device; receive quantitative analysis data of the sample from the device; compare said quantitative analysis received from the device with the predicted result; and update said data store with at least one of the output of the comparison and said received