Agent-Based Liver Inflammation Modeling for Patient-Specific Prognosis
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Solution Overview
Problem
Existing methods for modeling hepatic inflammation, fibrosis, and cancer are limited by the lack of accurate and comprehensive in silico systems, particularly due to the long-term nature of these diseases and the rarity of early-stage biopsy data, which hampers the development of realistic models for drug development and prognosis.
Innovation Solution
An agent-based computer-implemented modeling system simulates hepatic inflammation, fibrosis, and cancer using agents such as hepatocytes, macrophages, and cytokines like TNF-α and HMGB1, allowing for the prediction of disease progression and therapeutic effects on a patient-specific basis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If in vitro biological systems are used to model hepatic inflammation, then physical facilities are required and complexity is limited, but they cannot accurately model in vivo systems
Solution Approach 1:
The patent creates a virtual copy of the in vivo hepatic system through computer simulation. The in silico model replicates the complex biological interactions, cellular behaviors, and disease progression mechanisms that cannot be adequately represented in vitro, thereby achieving high modeling accuracy without physical constraints
Solution Approach 2:
The patent replaces physical biological systems (in vitro) with a computational information-based system. The mechanical and physical constraints of laboratory systems are substituted with mathematical models and algorithms that can simulate complex biological processes more accurately and flexibly
2Quantity of substance
If ordinary differential equations are used for modeling, then average concentrations are described well, but spatial resolution and individual cell behavior are lost
Solution Approach 1:
The patent segments the continuous biological system into discrete cellular agents, each representing an individual cell or molecular entity. This segmentation allows the model to track spatial positions and individual behaviors of cells while maintaining population-level concentration data, resolving the trade-off between averaging and spatial precision
Solution Approach 2:
The patent transitions from the continuous concentration field of ODEs to a discrete agent-based representation that adds spatial dimensionality. Each agent has explicit position coordinates, enabling the model to capture spatial heterogeneity and local interactions that are averaged out in traditional ODE formulations
3Loss of information
If liver biopsies are performed to obtain data, then early-stage disease data can be collected, but patient safety is compromised and data availability remains limited
Solution Approach 1:
The patent performs preliminary computational modeling and simulation to generate synthetic training data before actual clinical data collection. This preliminary in silico data generation reduces the need for invasive biopsies by providing sufficient training examples for machine learning models, thereby minimizing patient exposure to procedural risks
Solution Approach 2:
The patent introduces an in silico computational model as an intermediary between theoretical disease mechanisms and clinical observation. This virtual model translates biological knowledge into predictive simulations, reducing direct dependence on invasive clinical sampling while maintaining data quality for model training and validation
Data Source
AI summary
Provided herein are in silico methods of modeling hepatic inflammation, fibrosis/cirrhosis, and cancer. The models are computer-implemented agent-based models and are useful in determining patient prognoses in hepatic conditions, including viral infections, damage, inflammation, and cancer. The modeling system also is useful in modeling the effects of active agents on normal hepatic tissue or hepatic tissue perturbed by inflammation, infection, damage, fibrosis/cirrhosis, and cancer.