Gradient Boosting AUC Prediction for Immunosuppressants
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Solution Overview
Problem
Current methods for measuring the area-under-the-concentration-over-time (AUC) of immunosuppressants are cumbersome and lack accuracy due to the need for multiple blood samples, with existing regression methods providing insufficient precision.
Innovation Solution
A computer-implemented method using a gradient boosting technique, specifically extreme gradient boosting, to predict AUC values based on limited concentration data points, incorporating parameters such as concentration differences and covariates like time post-transplantation and immunosuppressant type, without requiring extensive datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple blood samples are taken during the dosing interval to measure AUC accurately, then measurement precision is improved, but device complexity and ease of operation deteriorate due to clinical impracticality
Solution Approach 1:
The patent extracts only the essential information needed for AUC calculation by using a limited number of strategically selected blood samples (e.g., trough level and one or two additional samples) rather than requiring multiple samples throughout the dosing interval. This extraction approach maintains acceptable measurement precision while dramatically improving ease of operation and clinical practicality.
Solution Approach 2:
The patent applies partial action by using fewer than the traditional multiple samples required for full AUC measurement. Instead of sampling at numerous time points, the method uses a partial set of samples (e.g., 2-4 samples) that, when combined with pharmacokinetic modeling and artificial intelligence algorithms, provide sufficient information to estimate AUC accurately, thereby improving ease of operation without sacrificing too much measurement precision.
2Measurement precision
If multiple blood samples are taken during the dosing interval to measure AUC accurately, then measurement precision is improved, but loss of time increases due to extended monitoring duration
Solution Approach 1:
The patent extracts the critical pharmacokinetic information needed for AUC estimation from a minimal set of blood samples taken at specific time points (e.g., trough level and one or two additional samples). This extraction strategy reduces the total monitoring time required while maintaining acceptable measurement precision through the use of pharmacokinetic modeling and artificial intelligence algorithms to interpolate the full AUC curve from the partial data.
Solution Approach 2:
The patent applies preliminary action by pre-establishing pharmacokinetic models and artificial intelligence algorithms that can rapidly estimate AUC from limited sample data. These pre-computed models allow for quick AUC calculation without requiring extended monitoring periods, thereby reducing loss of time while maintaining measurement precision.
3Ease of operation
If regression methods are used to reduce the number of blood samples, then ease of operation is improved, but measurement precision deteriorates due to insufficient accuracy
Solution Approach 1:
The patent replaces traditional mechanical regression methods with artificial intelligence techniques, specifically neural networks and gradient boosting algorithms. These AI systems learn complex non-linear relationships from training data and can accurately predict AUC from limited sample inputs, thereby maintaining measurement precision while improving ease of operation. The neural network model processes the limited blood sample data through multiple layers of computation to generate accurate AUC estimates without requiring the extensive sampling that traditional methods demand.
Solution Approach 2:
The patent changes the computational parameters and algorithms used for AUC estimation by transitioning from simple regression models to sophisticated artificial intelligence models. These AI models use multiple parameters including drug concentration, time post-administration, patient-specific factors, and pharmacokinetic parameters to generate accurate AUC predictions from limited data, thereby maintaining measurement precision while improving ease of operation.
4Measurement precision
If traditional AUC measurement methods are used, then measurement precision is improved, but device complexity increases due to multiple sampling requirements
Solution Approach 1:
The patent extracts the essential pharmacokinetic information needed for AUC measurement from a minimal set of blood samples taken at specific time points. By extracting only the critical data points (e.g., trough level and one or two additional samples) and using pharmacokinetic modeling with artificial intelligence algorithms, the system maintains measurement precision while dramatically simplifying the sampling system complexity and reducing the number of required samples.
Solution Approach 2:
The patent applies partial action by using fewer than the traditional multiple samples required for full AUC measurement. The method uses a partial set of samples (e.g., 2-4 samples at strategically selected time points) combined with pharmacokinetic modeling and AI algorithms to estimate the complete AUC curve, thereby reducing device complexity and sampling requirements while maintaining acceptable measurement precision.
Data Source
AI summary
The area under the curve (AUC) of an immunosuppressant is the best exposure marker for following the global exposure to an immunosuppressant in an organ of a subject. However, measuring AUC is complicated in routine care as it requires multiple blood samples during the dosing interval, which can be expensive and clinically impractical. The present invention proposes to use artificial intelligence technique to predict AUC values based on the value of some predictors. Such method conducts to surprisingly accurate results. Notably, the method enables to obtain satisfactory results with only two samples.