Neural Network Prediction of NK Cell Efficacy
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Solution Overview
Problem
The development of natural killer cell (NK) therapies is hindered by the significant variation in NK cell function between individuals and the time-consuming, costly process of in vitro culture and expansion required to achieve sufficient cell numbers for effective cancer treatment.
Innovation Solution
A method using a neural network model to predict the performance of NK cells by analyzing characteristic factors such as killer cell activating receptor (KAR) expression ratios, allowing for the evaluation of NK cell quality and potential therapeutic efficacy against specific cancer cells, thereby improving production efficiency and quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If in vitro culture and expansion is performed to achieve sufficient NK cell numbers, then the quantity of NK cells is improved, but the time consumption and cost increase
Solution Approach 1:
The patent applies preliminary action by performing functional analysis on a small number of initial NK cells before full-scale culture and expansion. This allows prediction of the NK cells' future performance and efficacy, enabling early selection of high-quality cell lines without waiting for the complete culture process to finish. The neural network model predicts killing results based on initial functional data, avoiding time loss from culturing all cell lines to completion before evaluation.
2Measurement precision
If comprehensive functional analysis is performed on NK cells, then the measurement precision of NK cell efficacy is improved, but the time consumption and material cost increase
Solution Approach 1:
The patent extracts the most critical functional parameter - the killing result against target cancer cells - from the comprehensive functional analysis. By focusing specifically on measuring NK cell killing activity rather than performing all possible functional assays, the method achieves sufficient measurement precision for efficacy prediction while significantly reducing analysis time and material consumption. The neural network model uses this extracted key parameter along with basic properties to predict overall NK cell performance.
3Reliability
If multiple NK cell lines are screened through functional analysis, then the reliability of selecting high-quality NK cells is improved, but the material cost increases
Solution Approach 1:
The patent uses copying by training a neural network model on data from a limited number of NK cell lines with comprehensive functional analysis. Once trained, the model can predict the performance of new NK cell lines based only on their basic properties (such as KAR expression levels), without requiring expensive functional analysis for each new line. This allows reliable screening of multiple NK cell lines while incurring material costs only for the initial training set and basic property measurements.
Data Source
AI summary
A method of predicting the efficacy of natural killer cells, including: generating a plurality of training data corresponding to a plurality of donors based on a characteristic factor and a corresponding killing result against the target cancer cells of a plurality of cultured natural killer cells from the donors; obtaining a trained neural network model by inputting the plurality of training data into a neural network model; inputting a to-be-tested input vector corresponding to at least one characteristic factor of a to-be-tested natural killer cell into the trained neural network model to obtain an outputted result vector of the trained neural network model, wherein the result vector indicates a predicted killing result corresponding to the target cancer cell after applying the to-be-tested natural killer cell; and determining a quality of the to-be-tested natural killer cell based on the predicted killing result.