PK/PD Linking Parameter Prediction Using Data Density Images
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Solution Overview
Problem
Conventional mathematical modeling methodologies for pharmacokinetic (PK) and pharmacodynamic (PD) evaluation are computationally intensive, time-consuming, and require significant human expertise, hindering their adoption for real-time applications and use by non-expert users.
Innovation Solution
A machine learning-based system transforms population PK and PD datasets into data density images, utilizing a deep learning system with neural networks to predict linking parameters between PK and PD effects, reducing the need for human intervention and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mathematical modeling methodologies are used for PK/PD evaluation, then model accuracy can be achieved, but computational time and resource requirements increase significantly
Solution Approach 1:
The patent pre-processes population PK and PD datasets into binned intensity images before modeling. This preliminary transformation organizes data into standardized visual representations that can be rapidly processed by machine learning algorithms, eliminating the need for repeated data preprocessing in iterative modeling cycles and significantly reducing computational time while maintaining accuracy.
Solution Approach 2:
The patent replaces conventional mathematical modeling algorithms (such as expectation-maximization and genetic algorithms) with machine learning-based image processing. This substitution transforms the mechanical iterative optimization process into a more efficient computational approach that processes pre-formed images, reducing computational burden and time requirements.
2Reliability
If conventional mathematical modeling methodologies are used for PK/PD evaluation, then model evaluation can be performed, but the process becomes time and labor intensive
Solution Approach 1:
The patent implements automated machine learning-based analysis that performs model evaluation without requiring extensive human intervention. The system autonomously processes binned intensity images, evaluates PK/PD relationships, and generates results, eliminating manual iterative refinement steps and significantly improving evaluation efficiency while maintaining reliability through consistent algorithmic processing.
Solution Approach 2:
The patent extracts the essential features from complex population datasets by transforming them into binned intensity images. This extraction process isolates the critical information needed for PK/PD evaluation into a simplified visual format, removing unnecessary data complexity and enabling faster, more efficient analysis while preserving the essential relationships needed for reliable modeling.
3Measurement precision
If conventional mathematical modeling methodologies are used for PK/PD evaluation, then comprehensive analysis can be achieved, but significant human expertise is required
Solution Approach 1:
The patent introduces binned intensity images as an intermediary representation between raw population datasets and final PK/PD analysis results. This intermediate visual format serves as a mediator that simplifies the complexity of the data, allowing machine learning algorithms to automatically extract meaningful relationships without requiring users to have deep expertise in mathematical modeling or data interpretation.
Solution Approach 2:
The patent replaces the need for expert human judgment and manual model refinement with automated machine learning-based image processing. The system autonomously performs comprehensive PK/PD analysis by processing binned intensity images, eliminating the requirement for users to possess specialized expertise in mathematical modeling methodologies while maintaining comprehensive and accurate analysis results.
Data Source
AI summary
A method and system for predicting a set of linking parameters that relate pharmacokinetic and pharmacodynamic effects. One or more processors receive a population dataset that comprises a population pharmacokinetic (PK) dataset and a population pharmacodynamic (PD) dataset. The one or more processors transform the population dataset into a plurality of data density images that includes a PK data density image and a PD data density image. The one or more processors predict the set of linking parameters using the plurality of data density images.