Predictive Immunity Profiling via Solution-Phase Interaction Analysis
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Solution Overview
Problem
Current methods for studying protein-protein interactions, such as ELISA and SPR spectroscopy, rely on surface immobilization, leading to false results and are unable to quantify biophysical parameters relevant to physiological processes, limiting the effectiveness of immunization strategies which are typically blanket and not tailored to individual immune responses.
Innovation Solution
A system that performs quantitative analysis of target/probe interactions in solution, using a microfluidic device and machine learning algorithms to create personalized predictive immunity profiles based on individual data, including biophysical properties and interaction characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surface immobilisation techniques (ELISA, SPR) are used to study protein-protein interactions, then sensitivity and measurement capability are improved, but false positive and false negative results occur due to non-specific surface interactions
Solution Approach 1:
The patent extracts the target proteins from the surface-immobilised state and studies them in their natural solution state. The system uses free-floating microparticles carrying target proteins that interact with probes in solution, eliminating the need for surface attachment and the associated non-specific interactions that cause false results.
Solution Approach 2:
The patent introduces an intermediary detection system using fluorescently labelled probes that bind to target proteins in solution. This intermediary approach allows indirect measurement of protein-protein interactions without direct surface contact, maintaining measurement sensitivity while avoiding surface-induced artifacts.
2Measurement precision
If surface-based assays are used for protein detection, then quantitative analysis capability is improved, but the ability to model physiological processes accurately is reduced because these processes occur in solution
Solution Approach 1:
The patent changes the physical state parameter of the assay system from surface-bound to solution-phase. By maintaining all components in solution with appropriate physiological buffers and conditions, the system achieves both quantitative measurement capability and physiological relevance, as the interactions occur in the same environment as in vivo conditions.
3Productivity
If blanket immunisation strategies are used based on population-wide modelling, then implementation efficiency is improved, but cost-effectiveness is reduced due to inability to account for individual immune response characteristics
Solution Approach 1:
The patent applies local quality by tailoring immunisation strategies to individual patients based on their specific immune response characteristics. The system measures and profiles each patient's immune responses to different pathogens and vaccines, then recommends personalised immunisation schedules and booster timings, rather than applying uniform population-wide protocols.
Solution Approach 2:
The patent performs preliminary immune profiling and predictive analysis before implementing immunisation decisions. The system uses machine learning models to predict future immune response decay and booster needs, allowing advance planning and optimisation of immunisation timing and strategy for each individual.
4Adaptability or versatility
If machine learning algorithms are used to analyse protein interaction data, then predictive capability is improved, but the ability to provide meaningful clinical outcomes is reduced due to limited binary output from current experimental techniques
Solution Approach 1:
The patent implements feedback by using machine learning models that continuously learn from measured protein interaction data and refine predictive accuracy. The system incorporates biophysical parameters such as binding affinities, kinetics, and concentrations into the learning process, generating increasingly accurate predictions of immune response outcomes that feed back into improved clinical decision-making.
Data Source
AI summary
A system is provided for developing a predictive immunity profile on the basis of the quantitative analysis of one or more samples from an individual. The system comprises: a device configured to perform quantitative analysis of the interaction between one or more target species and one or more probes in solution on a fluid sample to provided quantitative analysis data; a data store storing: personal data relating to at least one individual; at least one model of target/probe interaction; processing circuitry configured to access the data store and identify and retrieve data relevant to the sample; receive quantitative analysis data of the sample from the device; perform analysis to fit the model to the received quantitative analysis data; extrapolate, through the model, to create a predictive immunity profile for the individual, and update the data store with the quantitative analysis data.