Full Body Circulation Model for Drug Concentration Prediction

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Solution Overview

Problem

Current drug concentration models are inaccurate as they rely on predetermined parameters and only model single organs, failing to account for subject-specific characteristics and inter-organ interactions, which complicates drug metabolism prediction.

Innovation Solution

A system and method using a full body circulation model to predict drug concentrations in multiple organs by integrating subject and drug characteristics, adjusting blood flow rates, and modifying drug concentration prediction models based on subject-specific parameters, incorporating machine learning to iteratively refine model accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If predetermined parameters are used for drug concentration modeling, then the model can be simplified and easier to implement, but the accuracy of drug concentration prediction deteriorates due to inability to account for subject-specific characteristics

Engineering Contradiction:

ImproveEase of model implementationVSAvoidDrug concentration prediction accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent transforms fixed predetermined parameters into dynamic subject-specific parameters by incorporating machine learning models that adjust parameters based on individual subject characteristics (age, weight, organ volume, etc.). This allows the model to adapt parameters like partition coefficients and blood flow rates to each subject, resolving the contradiction between model simplicity and prediction accuracy.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces machine learning models as intermediaries between the physiological structure model and the drug concentration prediction. These ML models process subject-specific characteristics and generate optimized parameters, serving as a bridge that enables accurate personalized predictions without requiring complex direct measurements of difficult-to-obtain parameters like partition coefficients.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If single organ models are used, then the model complexity is reduced, but the ability to predict inter-organ interactions and overall drug metabolism deteriorates

Engineering Contradiction:

ImproveModel complexityVSAvoidDrug metabolism prediction reliability

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent merges multiple single-organ physiological models into an integrated full-body circulation model that simulates blood flow and drug distribution across multiple organs simultaneously. This combined model captures inter-organ interactions and overall drug metabolism while using a modular structure that manages complexity through systematic organization of organ models and blood flow connections.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal physiological structure model that can simulate multiple organs and their interactions within a single framework. This multi-functional model handles different drug types, administration routes, and subject characteristics through a unified approach, enabling comprehensive drug metabolism prediction without requiring separate models for each organ or scenario.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Ease of operation

If difficult-to-measure parameters like partition coefficients are not included, then the model becomes easier to implement, but the accuracy of drug concentration prediction in specific organs deteriorates

Engineering Contradiction:

ImproveModel usabilityVSAvoidOrgan-specific drug concentration accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces direct measurement of difficult physical parameters (like partition coefficients between venous blood and organs) with machine learning-based estimation. The ML models infer these hard-to-measure parameters from more easily obtained subject characteristics, substituting complex physical measurements with computational estimation that maintains accuracy while improving ease of use.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables the model to self-determine difficult-to-measure parameters by using machine learning algorithms that automatically calculate partition coefficients and other challenging parameters based on subject-specific data. The system serves itself by generating these critical parameters computationally rather than requiring external measurement, maintaining accuracy while simplifying operation.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10854326B2Systems and methods for full body circulation and drug concentration prediction

Publication Date: 2020.12.01 VERISIM LIFE INC

AI summary

A method for predicting drug concentration levels includes receiving at least one subject characteristic of the subject, and executing a full body circulation model by: determining a first concentration of the drug in a first blood flow entering a first organ determining a second concentration of the drug in the first organ, determining a third concentration of a drug in a third blood flow entering a second organ, the third blood flow downstream of the first organ, and determining, using the second organ model a fourth concentration of the drug in the second organ.