ODE-Based Neural Network for PK/PD Prediction
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Solution Overview
Problem
Conventional mathematical modeling techniques for pharmacokinetic and pharmacodynamic evaluations are labor-intensive and require significant human expertise, making them time-consuming and inaccessible to non-expert users, and struggle to accurately predict drug effects over time due to the complexity of data modalities in biomedical applications.
Innovation Solution
A neural network system incorporating ordinary differential equations (ODEs) is trained to predict pharmacokinetic and pharmacodynamic effects, allowing for the automation of model generation and prediction, reducing the need for human intervention and improving the accuracy and efficiency of drug effect modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mathematical modeling techniques are used for PK/PD evaluation, then model accuracy can be achieved through expert judgment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mathematical modeling with an automated neural network system that uses machine learning algorithms to predict PK/PD parameters. The system substitutes expert human judgment and iterative mathematical modeling with an automated computational approach that processes clinical trial data directly, eliminating the need for manual model building and parameter estimation while maintaining or improving prediction accuracy.
Solution Approach 2:
The neural network system performs self-training and self-optimization by automatically learning from clinical trial data without requiring continuous human intervention. The system autonomously adjusts its internal parameters and model structures based on the data it processes, enabling it to independently generate accurate PK/PD predictions without the iterative expert judgment required by conventional methods.
2Reliability
If conventional mathematical modeling techniques are used for PK/PD evaluation, then models can be built with human expertise, but the process requires significant know-how and computational resources
Solution Approach 1:
The patent replaces complex manual mathematical modeling processes with an automated neural network system that handles the computational complexity internally. The system substitutes the need for expert knowledge of differential equations and parameter estimation with a machine learning approach that automatically learns relationships from data, reducing the barrier to entry while maintaining model reliability through automated validation and optimization processes.
Solution Approach 2:
The neural network system is designed to handle multiple PK/PD modeling tasks and data types within a single unified framework. Rather than requiring separate expert models for different scenarios, the system provides a universal solution that can adapt to various clinical trial designs, drug types, and endpoints, simplifying the overall modeling process while maintaining reliability across diverse applications.
3Manufacturing precision
If conventional mathematical modeling techniques are used for PK/PD evaluation, then models can be developed through iterative refinement, but the process is inaccessible to non-expert users
Solution Approach 1:
The patent replaces the need for users to manually perform complex mathematical modeling with an automated system that handles all modeling operations. The system substitutes the requirement for user expertise in differential equations and parameter estimation with an automated interface that accepts clinical trial data and directly outputs PK/PD predictions, making the technology accessible to non-expert users while maintaining high precision through automated algorithms.
Data Source
AI summary
A method for predicting pharmacokinetic-pharmacodynamic effects over time is provided. A pharmacokinetic pathway of a neural network system that lies at least partially within an ordinary differential equations (ODE) module of the neural network system is trained to generate a dose effect output associated with a drug. A pharmacodynamic pathway of the neural network system that lies at least partially within the ODE module is trained to generate a drug effect output associated with the drug. The drug effect output associated with an administration of the drug over a time period is predicted using the neural network system.